Data Quality Issues and Solutions: Tackling the Challenges of Managing Data

Data Quality Issues and Solutions: Tackling the Challenges of Managing Data

by | May 10, 2023 | Data Quality

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As the world becomes increasingly data-driven, the importance of data quality cannot be overstated. High-quality data is critical to making accurate business decisions, developing effective marketing strategies, and providing high-quality customer service. However, data quality issues can significantly impact the accuracy of analyses and the effectiveness of decision-making. In this article, we’ll explore common data quality issues and how to tackle them through effective solutions and data quality checks.

Data-driven organizations depend on modern technologies and AI for their data assets. However, for them, the struggle with data quality is not unusual. Discrepancies, incompleteness, inaccuracy, security issues, and hidden data are only a few of the long list. Problems associated with data quality can cost companies a small fortune if not spotted and addressed.

Some common examples of poor data quality include… 

  • Wrong spelling of customer names.
  • Incompleteness or obsolete information of geographic locations.
  • Obsolete or incorrect contact details.

    Data quality directly influences organizational efforts leading to additional rework and delay. Poor quality data practices have a list of disadvantages like it undermines digital initiatives, weakening competitive standing, and directly affecting customer trust.  

Some common data quality issues

Poor quality of data is the prime enemy of machine learning used effectively. To make technologies like machine learning work data quality is a must on which we should pay attention. Let’s discuss what are the most common data quality issues and how they can be tackled.

1. Duplication of Data

Due to a massive influx of data from multiple sources such as local databases, cloud data lakes, streaming data, and the application and large system silos. This leads to a lot of duplication and overlaps in these sources. For instance, duplication of contact details ends up contacting the same customer multiple times. That can irritate the customer which can negatively affect the customer experience. On the other hand, some prospects are missed out as well. This can distort the results of data analytics.

Mitigation: Rule-based data quality management can be applied to keep a check on duplication and overlapping of records. We can define predictive DQ rules that learn from the data itself, are auto-generated, and improve continuously. Predictive DQ identifies fuzzy and identical data and quantifies it into a likelihood score for duplicate records. 

2. Inaccuracy in data

Data accuracy is vital in the industries like healthcare which are highly regulated. Inaccuracies prevent us from getting a correct picture and planning appropriate actions. Inaccurate customer data can disappoint a customer in personalized customer experiences.  

A number of factors such as human errors, data drift, and data decay lead to inaccuracies of data. According to Gartner, 3% of worldwide data gets decayed every month.  It causes data quality degradation and compromises data integrity. Automating data management can prevent such issues to some extent, but for assured accuracy, we need to employ dedicated data quality tools. Predictive, continuous, and self-service DQ tools can detect data quality issues early in the data lifecycle and also fix them in most cases.

3. Data ambiguity

After having taken every preventive measure to assure error-free data in large databases some errors will always sneak in such as invalid data, redundancy in data, and data transformation errors. It can get overwhelming for high-speed data streaming. Ambiguous column headings, lack of uniform data format, and spelling errors can go undetected. Such issues can cause flaws in reporting and analytics. 

To prevent such discrepancy issues predictive DQ tools must be employed which can constantly monitor the data with autogenerated rules, track down issues as they arise, and resolve the ambiguity.

4. Hidden data

Not all the data is used by organizations. Therefore many fields in the database are kept hidden. That creates large unused data silos.

So when the data is transferred or allowed access to new users the data handler may miss giving them access to the hidden fields.

This can deprive the new data user of some information that could be invaluable for their business. That can cause missing out on spotting new opportunities on many internal and external fronts.

An appropriate predictive DQ system can prevent this issue as it has the ability to discover hidden data fields and their correlations.

5. Data inconsistencies 

Data from multiple sources is likely to have inconsistencies in the information for the same data field across sources. There can be format discrepancies, unit discrepancies, spelling discrepancies, etc. Sometimes merger exercises of two large data sets can also create discrepancies. It’s vital to address these inconsistencies and reconcile them otherwise builds up a large silo of dead data. As a data-driven organization, you must keep an eye on possible data consistencies all the time.

We need a comprehensive DQ dashboard to automatically profile datasets, and highlight the quality issues whenever there’s a change in the data. And well-defined adaptive rules that self-learn from data and address the inconsistencies at the source, and the data pipelines only allow the trusted data.

7. Intimidating data size

Data size may not be considered a quality issue but actually, it is. Large sizes can cause a lot of confusion when we are looking for relevant data in that pool. According to Forbes, about 80% of the time business users, data analysts, and data scientists go into looking for the right data. In addition, other problems mentioned earlier get more severe in proportion to the volume of data.

In such a scenario when it’s difficult to make sense of the massive volume and variety of data pouring in from all directions, you need an expert such as [link to DataNectar] on your side who can devise a predictive data quality tool that can scale up with the volume of data, create automatic profiling, detect discrepancies, and changes in the schema, and analyze the emerging patterns.

8. Data downtime

Data downtime is a time when data is going through various transitions such as transformation, reorganizations, infrastructure upgrades, and migrations. It’s a particularly vulnerable time as the queries fired during this time may not be able to fetch accurate information. As a result of the database going through drastic changes, many things change and the addresses in the queries may not correspond to the previous data. Such updates and subsequent maintenance take up the significant time for the data managers.

There can be a number of reasons for data downtime. It’s a challenge in itself to tackle it. The complexity and magnitude of data pipelines add to the challenge. Therefore it becomes essential to constantly monitor data downtime and minimize it through automated solutions.

Here comes the role of a trusted data management partner such as [DataNectar] who can minimize the downtime while seamlessly taking care of the operations during the transitions and assure uninterrupted data operations.

9. Unstructured data

When information is not stored in a database or spreadsheet, and the data components can not be located in (a row, or column) manner, it can be called unstructured data. Some examples of unstructured data are descriptive text, and non-text content such as sound, video, picture, geographical, and IoT streaming info.

Even unstructured data can be rather crucial to support logical decision-making. However, managing unstructured data is a challenge in itself for most businesses. According to a survey by Sail Point and Dimensional Research, a staggering 99% of data professionals face challenges in managing unstructured data sets, and about 42% are unaware of the whereabouts of some important organizational information.

This is a challenge that can not be tackled without the help of intensive techniques such as content processing, content connectors, natural language understanding, and query processing language.

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How to tackle data quality issues? Solutions:

First, there is no quick-fix solution. Prevention is always better than cure even in this matter. When you realize that your data has turned into a large mess, the rescue operation is not going to be that easy. It should have prevented this from happening and therefore it is rather advisable that you have a data analytics expert like DataNectar on your side before implementing data analytics so that you can employ strategies to address data quality issues at the source.

It should be a priority in the organizational data strategy. The next step is to involve and enable all stakeholders to contribute to data quality as suggested by your data analytics partner.

Employ the most appropriate best tools to improve the quality as well as to unlock the value of data. Incorporate metadata to describe data in the context of who, what, where, why, when, and how.

The data quality tools should deliver continuous data quality at scale. Also, data governance and data catalog should be used to ensure access to relevant high-quality data in a timely manner to all stakeholders.

The data quality Issues are actually opportunities to understand their nature at their root so that we can prevent them from happening in the future. We must leverage data to improve customer experience, uncover innovative opportunities through a shared understanding of data quality, and drive business growth.

The data quality checks

The first Data Quality check is defining the quality metrics. Then identifying the quality issues by conducting tests, and correcting them. Defining the checks at the attribute level can ensure quick testing and resolution.

Data quality checks are an essential step in maintaining high-quality data. These checks can help identify issues with data accuracy, completeness, and consistency. 

The recommended data quality checks are…

  • Identifying overlaps and/or duplicates to establish the uniqueness of data.
  • Identifying and fixing data completeness by checking for missing values, mandatory fields, and null values.
  • Checking the format of all data fields for consistency.
  • Setting up validity rules by assessing the range of values.
  • Checking data recency or the time of the latest updates of data.
  • Checking integrity by validating row, column, conformity, and value.

Here are some common data quality checks that organizations can use to improve their data quality:

  • Completeness Checks
    Completeness checks are designed to ensure that data is complete and contains all the required information. This can involve checking that all fields are filled in and that there are no missing values.
  • Accuracy Checks
    Accuracy checks are designed to ensure that data is accurate and free from errors. This can involve comparing data to external sources or validating data against known benchmarks.
  • Consistency Checks
    Consistency checks are designed to ensure that data is consistent and free from discrepancies. This can involve comparing data across different data sources or validating data against established rules and standards.
  • Relevance Checks
    Relevance checks are designed to ensure that data is relevant and appropriate for its intended use. This can involve validating data against specific criteria, such as customer demographics or product specifications.
  • Timeliness Checks
    Timeliness checks are designed to ensure that data is up-to-date and relevant. This can involve validating data against established timelines or identifying data that is outdated or no longer relevant.

FAQs about data quality 

Q.1 Why is data quality important?

Data quality is critical because it impacts the accuracy of analysis and decision-making. Poor data quality can lead to inaccurate insights, flawed decision-making, and missed opportunities.

Q.2 What are some of the most common data quality issues? 

Some of the most common data quality issues include incomplete data, inaccurate data, duplicate data, inconsistent data, and outdated data.

Q.3 How can organizations improve their data quality?

Organizations can improve their data quality by developing data quality standards, conducting data audits, automating data management, training employees on data management best practices, using data quality tools, and implementing data governance.

Q.4 What are data quality checks? 

Data quality checks are a series of checks that are designed to ensure that data is accurate, complete, consistent, relevant, and timely.

Q.5 How often should data quality checks be conducted? 

Data quality checks should be conducted regularly to ensure that data quality is maintained. The frequency of checks will depend on the volume and complexity of the data being managed.

 

Q.6 What are some of the consequences of poor data quality? 

Poor data quality can lead to inaccurate analysis, flawed decision-making, missed opportunities, and damage to an organization’s reputation.


Conducting data quality checks at regular intervals should be mandatory to assure consistent business performance in any business. You should consider a proactive Data Quality tool that can report quality issues in real time and self-discovers the rules that adapt automatically. With automated Data Quality checks, you can rely on your data to drive well-informed and logical business decisions.

You can determine and set up your data quality parameters with the help of your Data Analytics partner and delegate this exercise to them so that you can focus on strategizing for business growth. This once again proves how important it is to have a Data Analytics partner like Data-Nectar who can take this responsibility freeing you from a hassle.

Conclusion

In conclusion, data quality is critical to making accurate business decisions, developing effective marketing strategies, and providing high-quality customer service. However, data quality issues can significantly impact the accuracy of analyses and the effectiveness of decision-making. By developing data quality standards, conducting regular data audits, automating data management, training employees on data management best practices, using data quality tools, and implementing data governance, organizations can tackle data quality issues and ensure that their data is accurate, complete, consistent, relevant, and timely. Regular data quality checks can also help organizations maintain high-quality data and ensure that their analyses and decision-making are based on accurate insights.

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Top Reasons to Outsource Data Analytics for Startups and Entrepreneurs

Top Reasons to Outsource Data Analytics for Startups and Entrepreneurs

by | May 3, 2023 | Data Analytics

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All business and IT operations, even critical ones like data science and analytics, are being outsourced by organizations.

For businesses that invest in digital growth, data science outsourcing is one of the most attractive fields in terms of competition.

Organizations can use the knowledge of skilled service providers who develop solutions, carry out analytics, and assist in the production of helpful business insights by outsourcing this area to a dependable supplier.

The tendency to allocate tasks to a data scientist or an entire team of specialists provided by an outside business has increased significantly over the past few years.

According to research, the size of the global market for data analysis would grow from 2018 to 2025 at a CAGR of more than 22.8%.

What is Data Analytics?

Data analytics is the science that enables a corporation to use raw data to generate insightful findings and recommendations that promote company growth.

A company can improve efficiency and performance in some areas, including marketing, logistics, finance, sales, and customer service, by using data analysis.

Data analytics assist a company in collecting data from multiple sources and seeing patterns that might lead to developing insightful information.

Organizations may use data analytics with the correct frameworks and structure to gain a competitive advantage.

Why Should You Outsource Data Analytics?

The age of outsourcing is currently in effect. All business and IT tasks, including strategic procedures, are being outsourced by companies.

Depending on the business, Data analytics outsourcing can be done for various reasons.

But it’s critical to understand that data currently strongly impacts how businesses function, and that importance will only grow.

As a result, data analytics must be taken into consideration by every business.

Businesses now use software systems that include cutting-edge technologies like digitization, machine learning, and AI.

Incorporating these systems from scratch can take time, effort, and money.

 

With data science outsourcing, any company may take full advantage of the rapidly changing technology trends and beat the competition.

Key Advantages Of Data Analytics Outsourcing

1. Access To Specialized Expertise

Prediction analytics, data visualization, and machine learning are just a few of the many divisions in the fast-expanding subject of data analytics. 

As a result, businesses may need help to stay up with the latest developments and patterns in data analytics.

Data analytics outsourcing can give businesses access to knowledge they might not have. 

For example, a business analytics services provider might include machine learning specialists that can assist a business in creating predictive models for identifying probable loss of clients. 

Alternatively, a service provider might have specialists in data visualization who can assist a company in developing user-friendly dashboards to present information to decision-makers.

2. Cost Savings

A full-time data analytics team’s hiring and upkeep can be costly. Organizations are required to give their employees perks, education, and tools in addition to pay. 

Also, because it could take time to discover the right people, employing an internal team can be time-consuming and expensive.

Outsourcing data analytics may be less expensive than hiring and training an internal team. 

Service providers frequently offer flexible pricing structures, enabling organizations to only pay for their required services. 

Instead of hiring a full-time employee, a company might contract a service provider to handle a one-time data analysis assignment. 

Long-term financial savings for companies are possible with this strategy.

3. Improved Efficiency

Business analytics services frequently have the tools and know-how to finish tasks more quickly and effectively than the in-house staff. 

A service provider may have access to specific software tools and data technology that can facilitate speedy and exact data analysis.

Based on the insights produced, outsourcing data analytics can assist businesses in making quicker and more informed decisions. 

It can be essential in finance, healthcare, and e-commerce, where choices must be made quickly.

4. Focus On Core Business Activities

It can take a lot of time and resources to analyze data. Instead of spending time and resources on data analysis duties, companies can concentrate on their core business operations by outsourcing data analytics. 

Instead of focusing on customer data analysis, an e-commerce business can create new items and enter new markets.

By outsourcing data analytics, the company may have more time and money to devote to its core operations. Organizations may benefit from this by achieving their objectives more swiftly and effectively.

5. Scalable

Different data analytics requirements may be required depending on an organization’s needs. 

For example, a company might require data analysis services during intense business activity but not in periods of low business activity.

Expert analytics services providers can quickly modify their offerings to suit the needs of the business. 

This adaptability enables firms to meet changing business needs without worrying about the time and expense required to acquire or fire internal staff.

6. Fast Results

Data analytics companies frequently have a bigger team and more resources available, allowing them to finish projects more quickly than their employees. 

Service providers can work on several at once or may have experts who can collaborate on a single project to complete projects more quickly.

Organizations can create insights and make data-driven choices more swiftly by outsourcing data analytics. 

For instance, data analytics services can finish the study more quickly, enabling the business to react to client demands and preferences more rapidly if it wants to analyze customer data to spot trends and patterns.

7. Greater Use of Data

With data’s increasing worth, it can be used effectively for company growth. 

The whole chain of information and analysis has experienced a significant change as machines have taken on the task of processing data. 

Therefore, for many companies wishing to use data more extensively in their operations, outsourcing data analytics is a necessity of the hour.

A qualified partner can raise a company’s commitment to managing its data and help it discover untested ways that may be important to long-term success.

8. Maintaining Compliance

Businesses must deal with various laws covering the collection, processing, storage, and use of data due to the growing volume of data. 

Your company will better understand and handle compliance obligations if you have a seasoned data analytics partner.

An external outsourcing partner can make it considerably easier for companies to produce easily audited data. 

A company must be on the correct side of the rules to ensure smooth operation, for example, with the General Data Protection Regulation (GDPR) and other equivalent versions in other markets.

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Risks Of Outsource Data Analytics

1. Risks to Data Security

When data analytics are outsourced, private information is available to a third party. Customer information, financial information, and other personal data may be included. 

As the outside party might have a different level of security standards in place than the business, this could pose risks to the safety of the data.

Organizations must thoroughly investigate the expert analytics services provider they hire to ensure they have adequate data security protocols in place to reduce this risk. 

Also, they must ensure that they have entered into suitable legal agreements with their service provider that contain provisions for data protection.

2. Quality Assurance Challenges

One can outsource data analytics by contracting with a third party to deliver improved insights and advice based on the data analysis. 

However, there is always a chance that the service provider’s work may need to measure up to the standards set by the company.

Organizations must set up clear quality control criteria and expectations with the analytics service provider to reduce this risk. 

To ensure the service provider meets their needs, they must also establish consistent channels of contact and feedback systems.

3. Cultural Differences

The organization and the supplier of services could face cultural hurdles due to outsourcing data analytics. It could result in errors of understanding, miscommunication, and inefficiency.

Organizations must create clear communication channels and protocols with their data analytics services to reduce this risk. 

Also, they must ensure that the service provider is well aware of the organization’s culture, beliefs, and objectives.

4. Control Loss

Outsourcing data analytics requires a certain amount of control over the analysis process being provided. 

Due to this, it could be more challenging to see and comprehend how the data is being processed and the results being made.

Organizations must establish transparent data analysis processes with their service provider to lessen this risk. 

To oversee the analysis process and guarantee that it meets their needs, they must also ensure they have access to the raw data and intermediate analysis outcomes.

Conclusion

Organizations can gain much from outsourcing data analytics, including access to specialist knowledge, cutting-edge technologies and infrastructure, and quicker outcomes. 

But it also has several dangers. The choice to outsource data analytics should ultimately be made after an in-depth assessment of the organization’s requirements, capabilities, and objectives. 

Organizations can use the potential of data analytics to fuel corporate growth, innovation, and success by carefully balancing the risks and rewards and choosing a reputable and skilled service provider.

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The Small Business Owner’s Guide To Data Analytics

The Small Business Owner’s Guide To Data Analytics

by | Apr 3, 2023 | Data Analytics

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Dear small business owners,

I hope that the oncoming depressing sentence doesn’t end up upsetting you but if it does, please bear with me because I empathize with you and want to suggest something which will not only be useful but it may open up a new horizon for your profitability.

In comparison with the large businesses, our position is quite vulnerable in multiple aspects such as financial standing and cash flow, strategy, client reliance, being updated with the market, leadership dependence, balancing quality and growth, access to cutting-edge technology, customer retention, supply chain management, inventory control, handling price fluctuations, employees evaluation, sensing a customer going away, control on operations, etc to name a few. As small businesses, we do want to help our customers and offer them great deals but there are limitations on how generous we can be to them.

In such a scenario there is a tool if applied correctly, effectively, and smartly can give us a great help to smartly counter the challenges posed by the mighty large-size competitors. That tool is Data Analytics.

What exactly is Data Analytics?

Data Analytics is a buzzword nowadays, and it’s also branded as ‘the next big thing.’

Data analytics is a set of processes to collect data from various sources, transforming that data using well-defined algorithms, and organizing it in a way that it reflects meaningful conclusions to support informed decision-making and also to predict future trends and patterns.

Companies collect large amounts of data every moment from many types of data-feeding sources such as mobile phone sniffers, loyalty cards, financial transactions, point of sales, web page visits, online purchases, social media interactions, text input on any net-connected devices, and literally from every device we can and can not think of.

But that data as it is collected in large amounts (which is called raw data) doesn’t make any sense unless we have those exceptional superhuman abilities (thank God we don’t have that.)

Therefore we need ‘something’ which can synthesize and analyze the raw data and derives some meaningful and useful information out of that intimidating jumble. ‘Data Analytics’ is ‘something’ that can do that for us. 

It’s an art or science which can create a picture of meaningful information for us from scattered jigsaw pieces called raw data. 

Data Analytics is a process of analyzing raw data to help us extract useful ‘insights’ which are not only important but inevitable to make business decisions.

Why should small business owners use data Analytics?

If I want to describe it in the shortest way possible, It’s “if you want to figure out how to provide exactly the right product at the right time, exactly to the right customer, data analytics is the tool you must use.”

Data analysis allows you to conduct an objective assessment of your business.

You have to use Data Analytics to give your customers better service, reward them for their loyalty, to offer them a supportive product/service for the product they are going to buy. And also to predict if they’re going away from you.

You can also predict what’s happening in your customer’s life by looking at the data of their buying behavior (for example if a customer starts purchasing nappies and infant milk powder, there has been the arrival of a baby in their life. So you can help them get those things that are necessary for their parenthood.)

As owners of small businesses, it is crucial to understand what all that data means and what messages that data is conveying to us. Only then we can make informed decisions that can lead the business to healthy growth. 

Here are some of the things small business data analytics can tell you:

  • Where your business stands now 
  • Where your company goes if the trends remain the same 
  • The growth potential of your business 
  • How long it should take to expand your brand 
  • The steps to take to make the expansion happen

Note:

  1. The data must be analyzed daily, weekly, monthly, quarterly, and annually to get answers to all these questions.

     

  2. But one problem is the struggle to understand data. But we will see in the coming pages that it’s also not an issue provided you have a trusted pair of hands on which you can rely to take care of all the data-related operations. 

What are the benefits of Data analytics?

Data can point us in the right direction, and prevent us from getting in the wrong direction by showing us objective and unbiased facts.

  • For instance, by looking at the crowd in a giant supermall one can be tempted to get a place in the same supermall. But the reality can only be reflected when we look at the numbers of people visiting and people actually spending money there, and what is selling in what amount.

We can study the trends through various patterns in the data that help us in describing what happened, diagnose the exact issues, predict how the market will behave, and prescribe appropriate actions to deal with or take optimum advantage of the oncoming trends.

We can adapt data-directed thinking processes and decision-making.

If we want to understand our customers and figure out better and more profitable ways to help them, and also understand the behavior of our own organization to make it more operationally efficient, we certainly need to give data strategic importance.

We can logically devise strategies for expansion.

Other than these, there are a few more ways we can also apply Data Analytics to gain many other benefits. Some of them are as follows.

  1. It helps us reduce costs by shortening tasks, and in many cases eliminating them altogether.
  2. Organizational efficiency can be increased significantly by Increased operational efficiency.
  3. Data doesn’t lie. It helps us identify the exact weaknesses and failures.
  4. We can design new products and services based on Predictive Analysis and Prescriptive Analysis.
  5. Data can give us 360-degree customer reviews so we don’t have to rely on subjective spot surveys.
  6. Through thoroughly conducted Data Analytics it becomes easy to spot leakages which makes it easy to identify and prevent fraud.
  7. Data Analytics can also help us optimize pricing strategies.

In short, Data Analytics helps us make smarter logical business decisions.

Which BI Tools / Which technology tool should they select?

There are a number of BI tools to choose from, with different specialties. So it depends on your business needs and which functions of a BI tool you want to employ. Are your needs basic, or do they demand complex analysis? However, these are the basic criteria one should consider to select the best fit for one’s business.

 

  • Capabilities to collect data collection
  • Analytical abilities
  • Visualization facilities
  • Customizable reporting tools
  • Customizable dashboards
  • Predictive analytics
  • Integration ability with other tools
  • Security 

Here we are briefly explaining a few good BI tools. If you wonder which would be a better option for your business needs, please contact us for the best data analytics services and solutions. They will be happy to support you in choosing the right Data Analytics tool for you.

  1. Microsoft Power BI enables you to transform, explore, and analyze data on-premise and in the cloud. Also, it creates real-time visualizations and can connect relatively easily to your own data sources.

     

  2. Zoho Analytics has perhaps the most beautiful interactive dashboard. It supports multiple source data collection, and the data can be easily integrated through a simple interface and exports the results to various platforms and ecosystems.

     

  3. Scoro is good for its customizable KPI dashboard and real-time overview of every aspect of your work.

     

  4. Dundas BI is an end-to-end business intelligence platform with an open API across the entire platform. With drag-and-drop tools, it can quickly transform raw data into the form of dashboards, reports, and visual data analytics. Its ability to connect and integrate with other data sources is remarkable.

     

  5. Sisense can incorporate AI-enabled applications that can be embedded and integrated with a wide variety of sources and doesn’t require specialized training. It can get real-time data feeds to create intuitive dashboards and reports.

     

  6. MicroStrategy supports both data mining and visualization. It offers a multi-functional dashboard, big data solutions, and advanced analytics.

     

  7. Halo combines automated data processes with manual data manoeuvres for custom results. Its data integration, supply chain analytics, and visualization are automated and available in a single solution. For supply-chain management, this is most suited. Its intuitive interface allows multiple users to collaborate in real-time.

     

  8. Oracle has a large array of BI capabilities. It uses the Common Enterprise Model for calculations and business analytics and offers inbuilt tools for mining data, sending alerts, and data discovery which is rather agile. Its workspace is also easy to use and allows multi-user collaboration.

     

  9. SeekTable can perform ad-hoc analysis of all multiple sources of business data at once. It comes with facilities such as data restriction, live interactive reports, sorting, filtering, etc. It offers data analytics while allowing users access to reports.

     

  10. Tableau is a long-time tried and tested BI tool for live visual analytics. Its highly intuitive interface and drag-and-drop facility allow users to observe live trends. It features a mobile BI strategy and in-memory architecture for data visualization and exploration. It’s easy to integrate with Microsoft SharePoint and offers one-click reporting.

     

  11. GROW allows the extraction of data from over 115 sources, including Dropbox, Salesforce, Twitter, Google Analytics, etc. It features a highly intuitive UI with several data visualization elements. It also facilitates importing data from social media platforms such as Facebook, Twitter, LinkedIn, and more, helping optimize the marketing budget.

     

  12. Datapine facilitates and allows the visualization of many key metrics simultaneously. It’s an interactive BI tool featuring enabling versatile filters, mobile optimization, ad-hoc data source queries, fast and efficient connections to multiple data sources, predictive analytics, and data alarms based on customizable triggers. 

Other than these there are many more intelligent BI tools such as Syn Enterprise, BigID, Qualtrics Research Core, Active Batch, Salesforce Analytics Cloud, Board, CXAIR Platform, Looker, Reveal, Yellowfin, Periscope Data, AnswerDock, etc.

If you are looking for the best fit BI tool for your business, the best course of action is to talk to an expert at DataNectar who will understand your business, its processes, and your objectives and then figure out which one will be the best option.

Which types of skill sets do small business owners need for their organization?

To be very frank, you don’t need any skills to employ data analytics. This may come as a shock but think about it.

I’m sure you have heard this famous proverb “Do your best and delegate the rest.”

That’s the way forward to progress and growth. If you end up doing everything yourself, when will you think about expanding your business? To paraphrase Michael Gerber, if you end up working ‘in’ your business, when will you work ‘on’ your business?

Therefore the best answer I can give to this question is “Have a BI & Data Analytics partner like DataNectar on your side to take care of your BI needs.”

Having said that, let’s as well discuss what types of skills can be helpful to take optimum advantage of Data Analytics.

  • SQL (Structured Query Language) – It’s a programming language widely used for databases.
  • Oracle – It is a database commonly used for running online transaction processing, data warehousing, and mixed (OLTP & DW) database workloads.
  • R and Python – These are the most popular statistical programming languages used to create advanced data analysis programs
  • Machine Learning – an aspect of artificial intelligence that uses algorithms for pattern recognition in data
  • Statistical skills such as calculating probability to be able to analyze and interpret data trends
  • Data management – proficiency in collecting, organizing, and storing data
  • Data visualization – competence to visualize and illustrate data through graphic aids such as charts, graphs, and various figures
  • Econometrics – the skill to create mathematical models from the data trends that can predict future trends
  • Mathematical & statistical ability
  • Soft-skills:
    • Analytical mindset – An analyst must be able to analyze the data from multiple points of view to understand what’s happening and to dig deeper if necessary.
    • Problem-solving skills: Data analytics is all about answering questions and solving business challenges, and that requires some keen problem-solving skills. Data analysts have a wide variety of tools and techniques at their disposal, and a key part of the job is knowing when to use what. 
    • Communication skills: Once you’ve harvested your data for valuable insights, it’s important to share your findings in a way that benefits the business. Data analysts work in close collaboration with key business stakeholders and may be responsible for sharing and presenting their insights to the entire company. So, if you’re thinking about becoming a data analyst, it’s important to make sure that you’re comfortable with this aspect of the job.

What will be the role of a Data Analyst in your organization?

A data analyst collects all the scattered pieces of a large complex jigsaw data puzzle and creates a meaningful picture so that others can use that information. So if you choose to employ a full-time Data Analyst, his/her responsibilities will be like these.

 

  • To manage the delivery of user behavior surveys and create reports based on the results.
  • Work with clients to develop requirements, define success metrics, manage and execute analytical projects, and evaluate results.
  • Monitor practices, processes, and systems to identify opportunities for improvement.
  • Coming up with good questions and translating them into well-defined analytical tasks.
  • Gather new data to answer client questions, collating and organizing data from multiple sources.
  • Devise, build, test, and maintain back-end code.
  • Establish data processes, define data quality criteria, and implement data quality processes.
  • Work as part of a team to evaluate and analyze key data that will be used to shape future business strategies.

As a business leader, it must be an obvious matter for you to be aware of how crucial thing Data Analytics is. Also how vast a subject it is, and what level of complexities it involves. Therefore you must have employed a proper Data Analytics system and experts to run that system. 

However, the subject being a relatively recent phenomenon, it’s far from being practical that every organization would have its own team of Data Analytics experts.

Therefore it’s wise to have an external partner like us to direct and manage this matter.

We, at DataNectar, have a team of veterans who understand not only Data Science & Engineering but also the business processes in multiple industries thoroughly. They will be able to objectively study your business and come up with the parameters for analysis and also devise appropriate algorithms to extract information from the data.

We employ a system to extract the data from multiple data sources.

Clean the data up and store them in a defined order in a warehouse.

Take the data through a transformation procedure. And Create various visually understandable dashboards and analyses. 

By the way, we will choose the best tools for different steps in the entire BI exercise, and also set up automation wherever required.

After having done this exercise,

We sit with the team of the leaders of your business to support the brainstorming for interpreting the analysis.

We also support brainstorming for predicting the oncoming trends and Strategizing to take optimum advantage of those trends. 

At this stage, we’d like to offer you a free consultation for 15 minutes over the phone to understand your business issues. At the end of that conversation, either party can decide whether we are fit to work together or not. If we feel there is a synergy, we can set up a time for the next meeting, and if we don’t, we can still be friends. 

Feel free to contact us at [email protected] or visit our website at www.data-nectar.com

A look into Snowflake Data Types

A look into Snowflake Data Types

by | Feb 10, 2023 | Business Intelligence

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As a Database as a Service (DBaaS), Snowflake is a relational Cloud Data Warehouse that can be accessed online. This Data Warehouse can give your company more leeway to adapt to shifting market conditions and grow as needed. Its Cloud Storage is powerful enough to accommodate endless volumes of both structured and semi-structured data. As a result, information from numerous sources can be combined. In addition, the Snowflake Data Warehouse will prevent your company from needing to buy extra hardware.

Snowflake allows you to use the usual SQL data types in your columns, local variables, expressions, and parameters (with certain limitations). An identifier and data type will be assigned to each column in a table. The data type tells Snowflake how much space to set aside for a column’s storage and what form the data must take.

Snowflake’s great global success can be attributed to the following characteristics: 

    • Snowflake’s scalability stems from the fact that it provides storage facilities independent of its computation facilities. Data is stored in a database and processed in a virtual data warehouse. As a result, Snowflake guarantees excellent scalability at a low cost.
    • Snowflake requires little upkeep because it was made with the user in mind. It has a low barrier to entry and needs little in the way of upkeep.
    • Automated query optimization is supported in Snowflake, saving you time and effort over the hassle of improving queries manually.
    • Snowflake allows you to divide your company’s regular workloads into different virtual Data Warehouses. As a result, this facilitates Data Analytics management, particularly under extremely regular loads.

Six Important Snowflake Data Types

The first step in becoming a Snowflake Data Warehouse expert is learning the ins and outs of the different types of data it stores. There are 6 different kinds of data that can be used with Snowflake.

    1. Numeric Data Types
    2. String & Binary Data Types
    3. Logical Data Types
    4. Date & Time Data Types
    5. Semi-structured Data Types
    6. Geospatial Data Types

1) Numeric Data Types

Knowing what precision and scale are is crucial before diving into the various sorts of numeric data types. 

    • A number’s precision is the maximum number of significant digits that can be included in the number itself.
    • Scale is the maximum number of digits that can be displayed following a decimal point.

Precision has no effect on storage; for example, the same number in columns with different precisions, such as NUMBER(5,0) and NUMBER(25,0), will have the same storage requirements. However, the scale has an effect on storage; for example, the same data saved in a column of type NUMBER(20,5) requires more space than NUMBER(20,0). Additionally, processing bigger scale values may take a little more time and space in memory.

So here are a few types of numeric data types:

    • NUMBER is a data type for storing whole numbers. The default scale and precision settings are 0 and 38, respectively.
    • DECIMAL and NUMERIC are the same as NUMBER.
    • The prefixes INT, INTEGER, BIGINT, and SMALLINT all mean the same thing as NUMBER. But you can’t change the scale or precision; these serial data types are permanently stuck at 0 and 38.
    • Snowflake uses double-precision IEEE 754 floating-point values (FLOAT, FLOAT4, FLOAT8). 
    • FLOAT is a synonym for DOUBLE, DOUBLE PRECISION, and REAL.
    • Numeric Constants are numbers that have fixed values. It supports the following format:

2) String & Binary Data Types

The following character-related data types are supported in Snowflake:

    • With a maximum size of 16 MB, VARCHAR can store Unicode characters of any size. There are BI/ETL tools that can set the maximum allowed length of VARCHAR data before storing or retrieving it.
    • CHARACTER, CHAR is like  VARCHAR, but with the default length as VARCHAR(1).
    • If you’re familiar with VARCHAR, you’ll feel right at home with STRING.
    • Just like VARCHAR, TEXT can store any kind of character.
    • The BINARY data type does not understand Unicode characters; hence its size is always expressed in bytes rather than characters. There’s an upper limit of 8 MB.
    • To put it simply, VARBINARY is another name for BINARY.
    • String Constants are fixed values. When using Snowflake, string constants must always be separated by delimiter characters. Delimiting string literals in Snowflake can be done with either single quotes or dollar signs.

3) Logical Data Types

In logical data type, you can only use BOOLEAN with one of two values: TRUE or FALSE. Sometimes it will show up as NULL if the value is unknown. The BOOLEAN data type offers the necessary Ternary Logic functionality.

SQL requires using a ternary logic, often known as three-valued logic (3VL), which has three possible truth values (TRUE, FALSE, and UNKNOWN). To indicate the unknown value in Snowflake, NULL is used. The outcomes of logical operations like AND, OR, and NOT are affected by ternary logic when applied to the evaluation of Boolean expressions and predicates.

    • UNKNOWN values are interpreted as NULL when used in expressions (like a SELECT list).
    • Use of UNKNOWN as a predicate (in a WHERE clause, for example) always returns FALSE

4) Date & Time Data Types

This details the date/time and time data types that can be managed in Snowflake. It also explains the allowed formats for string constants to manipulate dates, times, and timestamps.

    • The DATE data type is supported in Snowflake (with no time elements). It supports the most typical dates format (YYYY-MM-DD, DD-MON-YYYY, etc.).
    • DATETIME is shorthand for TIMESTAMP NTZ.
    • A TIME data type represented as HH:MM: SS is supported by Snowflake. Additionally, a precision setting for fractional seconds is available. The default precision is 9. The valid range for All-TIME values is between 00:00:00 to 23:59:59.999999999. 
    • An alternative name for any of the TIMESTAMP_* functions is TIMESTAMP, which can be set by the user. The TIMESTAMP_* variant is used in place of TIMESTAMP whenever possible. This data type is not stored in tables.
    • Snowflake supports three different timestamp formats: TIMESTAMP LTZ, TIMESTAMP NTZ, and TIMESTAMP TZ.

       

      • The TIMESTAMP LTZ function accurately records UTC. The TIMEZONE session parameter determines the time zone in which each operation is executed.
      • TIMESTAMP NTZ accurately records wallclock time. Without regard to local time, all tasks are carried out.
      • By default, TIMESTAMP TZ stores UTC time plus the appropriate time zone offset. The session time zone offset will be utilized if the time zone is not specified.

5) Semi-Structured Data Types

Semi-structured data formats, such as JSON, Avro, ORC, Parquet, or XML, stand in for free-form data structures and are used to load and process data. To maximize performance and efficiency, Snowflake stores these in a compressed columnar binary representation internally.

    • VARIANT is a generic data type that can hold information of any other type, including OBJECT and ARRAY. Its 16 MB of storage space makes it perfect for archiving large files.
    • OBJECT comes in handy to save collections of key-value pairs, where the key is always a non-empty string and the value is always a VARIANT. Explicitly-typed objects are currently not supported in Snowflake.
    • Display both sparse and dense arrays of any size with ARRAY. The values are of the VARIANT type, and indices can be any positive integer up to 2^31-1. Arrays of a fixed size or containing values of a non-VARIANT type are not currently supported in Snowflake.

6) Geospatial Data Types

Snowflake has built-in support for geographic elements like points, lines, and polygons. The GEOGRAPHY data type, which Snowflake provides, treats Earth as though it were a perfect sphere. It is aligned with WGS 84 standards.

Degrees of longitude (from -180 to +180) and latitude (from -90 to +90) are used to locate points on Earth’s surface. As of right now, altitude is not a supported option.  More so, Snowflake provides GEOGRAPHY data-type-specific geographic functions.

Instead of retaining geographical data in their native formats in VARCHAR, VARIANT, or NUMBER columns, you should transform and save this data in GEOGRAPHY columns. The efficiency of geographical queries can be greatly enhanced if data is stored in GEOGRAPHY columns.

The following geospatial objects are compatible with the GEOGRAPHY data type:

    • Point
    • MultiPoint
    • MultiLineString
    • LineString
    • GeometryCollection
    • Polygon
    • MultiPolygon
    • Feature
    • FeatureCollection

Unsupported Data Types

If the above list of SQL server data types is clear, then what is the type of data that is incompatible with Snowflake? Here is your answer.

  • LOB (Large Object) 
    • BLOB: You can also utilize BINARY, with a maximum size of 8,388,608 bytes. 
    • CLOB: You can also use VARCHAR, with a maximum size of 16,777,216 bytes (for a single byte).
  • Other
    • ENUM
    • User-defined data types

Conclusion

While your primary focus should be on learning how to use customer data, you may be questioning why it’s necessary to know so many different data types. There is one motive for doing this, and that is to amass reliable information. Data collection and instrumentation aren’t the only areas where you can use your data type knowledge; you’ll also find that data administration, data integration, and developing internal applications are much less of a challenge now that you have a firm grasp on the topic.

Also, without a good database management system, it is impossible to deal with the massive amounts of data already in existence. Get in touch with our experts for more information.

How to Choose the Best Data Visualization Tools

How to Choose the Best Data Visualization Tools

by | Jan 30, 2023 | Data Visualisation

Data is getting immense with every passing year and in nearly all industries. As metrics pile up considerably, you, as an organizational decision-maker, may find yourself confused about which data points collected are essential and in what approaches they can assist your business operations. 

All of this data is tough for the human brain to grasp. It is tricky to comprehend numbers more significant than five for a human brain without sketching some abstraction. Data visualization professionals can play a vibrant role in generating those abstractions.

Big data is impractical if it can’t be understood and digested conveniently. That is why data visualization plays a significant role in the whole thing, from economics to technology, enabling decision-makers in IT companies and end users of BI technologies like hospitals and industries like manufacturing.

By converting multifaceted numbers and other pieces of data into visual elements, content becomes simpler to comprehend and use in diverse applications.

So, here, you require data visualization techniques and need to select the best tools that can maximize your utilities.

What is Data Visualisation?

Data visualization in simple terms is an arrangement of visual elements of a set of data that is highly interactive, intuitive, personalized, and easy to share. 
For instance, text-based data is visualized graphically in the outline of charts, graphs, tables, Infographics, and maps to analyze business or operational scenarios. 

So, by manipulating big data sets in the form of visual formats, you can clearly understand the story your data depicts at a swift glance, instead of working on piles of tables and numbers for long hours.

How does it Enable Business Intelligence Dynamics?

Now coming to the context of Business Intelligence (BI) dynamics, data visualization is used and applied in two ways. 

Data is visualized in form of Dashboards that represent business data from every angle by allowing one to measure its performance in any dimension. Data can be drilled down and dissected any information. We can slice & dice the information in any unit size.

Do you want to know what valuation Business Intelligence (BI) can bring to your organization?

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Data Visualisation can Assist your Organisation with Diverse Approaches

How does data visualization help decipher digital information?

Large and ever-altering quantities of data related to your business’s health, such as customer interactions, user experiences, staff performance levels, and expenditures can robustly impact and influence the overall decision-making at crucial moments. However, this is only probable when such data is clearly understandable even by non-data professionals.

With data visualization, you can translate scores of text and numbers to instinctively understandable insights. A step further, visualization tools can transform raw metrics into insightful stories that can be easily shared and worked upon.

How can data visualization help discover trends swiftly?

Data visualization facilitates your organization to spot alterations in customer behavior and market conditions swiftly. For instance, by utilizing heat maps, one can rapidly spot expansion opportunities, which is not evident in spreadsheets. 

On the other hand, Radius maps enable you to focus on spatial relationships for realizing enhanced business efficiencies or oversupply.
Further, with territory mapping, your sales teams can easily view their territories and ensure they are aligned or not.

How does data visualization help with decision analysis?

When you feed precise and neutral data visualizations into the decision-making tools, you can make enhanced decisions for your organization. Accurate data visualizations don’t deform the original information with unreliable displays. 

Additionally, charts and dashboards should be updated with dynamism using the newest information keeping the decision-making analysis highly applicable and relevant.

How data visualization reveals flaws, fraud, and anomalies?

Erroneous data can lead to a severe threat to businesses that depend on their correctness and accuracy levels. Data visualizations like charts and graphs can swiftly highlight large discrepancies in data readings, specifically signaling, where more careful reviewing of the numbers may be crucial.

Identifying and visualizing data patterns

Data visualization software enables you to identify and visualize data patterns with relationships that occur amid daily operations and overall business performance. 

However, you should be cautious of inappropriate comparative visualizations as if your organizational data analysis is puzzling or tough to compare; your visualizations might be doing more damage than enhancements.

Following are two charts that illustrate: 
a) Poor Data Visualisation,
b) Enhanced Data Visualisation through Dashboard.

a) Poor Data Visualisation: 

b) Enhanced Data Visualisation through Dashboard.

Let us further explore the bad data visualization and good data visualization examples in detail.

Example of Bad Data Visualization 

#1: Pie chart with multiple categories

bad data pie chart

Pie charts are leveraged when 2 to 3 product items make up the complete data set. Any more than that, and it is tough for the human eye to differentiate between the parts of a circle.

Notice how difficult it is to differentiate the size of these diverse parts. 

What is the exact difference between India and Russia?

It is rough to calculate the exact size difference. Rather, substitute this with a bar chart.

Example of Good Data Visualization: Precise Bar Chart

good data bar chart

Here you can explicitly calculate the difference between India (6.80%) and Russia (4.90%).

Bar charts will be your go-to option for exact data visualization.

7 Best Data Visualization Tools Which Are Popular In 2022-23

1. Power BI

Power BI is effortless to set up with dashboards and data connectors to on-premise and cloud-based sources such as Salesforce, Azure SQL DB, or Dynamics 365. The open framework enables the creation of custom visuals. 

It possesses default data visualization elements with bar charts, pie charts, maps, and even complex models like waterfalls, funnels, gauges, and other components. 

Power BI is developed and enabled with machine learning abilities, so it can automatedly spot patterns in data using them to make informed predictions through “what if” scenarios. These estimates facilitate users to make forecasts and meet future demands or significant metrics. 

A user can easily save his work to a file, and publish data and reports through Power BI to share with other stakeholders. Power BI is utilized to develop custom dashboards as well as reports as per the relevancy and access of data. 

Through custom visuals SDK, one can generate stunning visualizations, based on rich JavaScript libraries like D3, jQuery, and R-language scripts.

You also might like to read more about our best case study which is Remodelling advertising pricing strategy with Data Analytics 

 

2. Tableau

Tableau has an extensive customer base of more than 57,000 accounts because of its capability to generate interactive visualizations far beyond those offered by standard BI solutions. 

It is best for managing massive and quickly altering datasets utilized in Big Data operations, machine learning, and artificial intelligence applications. Further, it can be integrated with modern database solutions including Amazon AWS, Hadoop, My SQL, Teradata, and SAP.

Developing content in Tableau doesn’t need conventional source control or dev-test-prod-related techniques. You can integrate Tableau content development and deployment into your present development systems.

Publishing data to Tableau is integral to sustaining a single source for accessible data. Publishing facilitates sharing data with colleagues; even those not using Tableau Desktop, however, have required editing permissions. 

The top features of Tableau include Tableau Dashboard, Collaboration and Sharing, Live and In-memory Data, Data Sources, Advanced Visualizations (Chart Types), Maps, Mobile view, and robust security. D3.js is an exclusive JavaScript library that is utilized for Tableau data visualization.

3. MicroStrategy

MicroStrategy provides intuitive tools with data discovery and big data analytics features with an extensive library to visualize data. 

The MicroStrategy platform backs engaging dashboards, scorecards, advanced reports, thresholds, alerts, and automated report distribution. The tool can connect to over 200 data sources which include RDBMS, Cloud data, OLAP, and Big data.

Dossiers are MicroStrategy’s advanced and modern dashboards. To make the dossier to be presentation-ready, one requires to certify it to validate that the content is trustworthy. Once certified, you can share it with the enterprise environment for collaboration and publishing.

MicroStrategy Library is a unique and personalized virtual bookshelf that enables you to access dossiers from one common location. Through the MicroStrategy library, you can reach out to subject matter specialists and have a conversation regarding your data visualizations.

4. Qliksense

The vendor has 40,000+ customer accounts across 100+ countries, offering a highly adaptable setup and extensive features. 

Along with its data visualization abilities, the Qliksense tool even provides business intelligence, and enables the storytelling capacity of dashboards, data analytics, and reporting with a sleek user interface. 

There is also a sturdy community and 3rd party resources obtainable online to assist fresh users in understanding how to incorporate it into their current projects.

The Qliksense dashboard is an influential feature to showcase values from multiple fields simultaneously, and its functionality of data association in memory can showcase the dynamic values in all the available sheet objects. 

Qlik DataMarket® is an integrated data-as-a-service (DaaS) of Qlikview offering an all-inclusive library of data sets from reliable sources. Qliksense developers can use the same and effortlessly enable their analyses with external data sets to have an “outside-in” perspective for deeper insights.

5. Google Data Studio

Google Data Studio is a tool that enables communication and acts on tailored data sets. Programmers, executives, and worldwide team members from diverse departments can match, filter, and well-organize the precise data sets they require swiftly in one single report. No more waiting for numerous and static data reports to fill their inbox.

Data Studio is now an integral part of Google Cloud’s BI solutions. By blending Data Studio with Looker, Google Cloud has the finest of both ends – a structured semantic model and a self-served, simple-to-use front-end app with Data Studio that enables the analysis of unstructured/ungoverned data sets.

6. Apache Superset

Apache Superset is an advanced exploration and data visualization platform. It can substitute or enhance proprietary BI tools for many teams. It blends well with a diversity of data sources.

It offers a no-code interface for swiftly crafting charts. It provides a powerful web SQL Editor for progressive querying and a lightweight semantic layer for rapidly defining custom dimensions and precise metrics.

It provides an extensive array of attractive visualizations to display your data sets, ranging from straightforward bar charts to geospatial visualizations.

7. Looker

Looker Studio is a self-service BI with unmatched suppleness for intelligent business decisions. It helps tell powerful stories by building and sharing interactive reports and data visualizations. 

It assists in transforming your data sets to business metrics and dimensions with intuitive, intelligent reports. The tool enables professionals with significant business metrics by sharing automated dashboards. It helps you generate shareable, tailored charts and graphs with merely a few clicks.

Moving Forward

Extract, transform & load (ETL) are 3 data processes, enabled after data collection. 

Extraction takes data, collected in varied data sources with diverse structures and formats, to the staging database. 

Transformation takes fetched data and applies predefined rules to it, and load takes the transformed data and stores it in Data Warehouse (DW). 

However, this data is multifaceted until it is parsed and showcased in a simplified way. 

Specialists at Data Nectar enable the seamless consumption of significant insights by transforming the data analysis into visual representations with the assistance of Reports and Dashboards to decipher trends, anomalies, and data usage patterns.

At Data Nectar, a data analytics and visualization technology company, we know the real significance of Data Visualization for multiple stakeholders, and we can assist you in choosing precise tools in line with your requirements. 

Further, we enable SMEs and Enterprises with analytics-driven technology solutions to realize enhanced performance and maximize ROI in the process – through data. 

If you all too, as your organization’s decision-makers are willing to discover the vast possibilities Data can bring to your business or industry operations, Call Us Today!