Top Strategies and Tools for Data-Driven Decision-Making Business Intelligence

Top Strategies and Tools for Data-Driven Decision-Making Business Intelligence

In the modern business climate, data-driven decision-making is taking on an increasingly significant role. Recent data show the significant benefits of using this method. Forbes found that organizations that use data-driven insights boost productivity and profits by 5-6% on average.

In addition, research conducted that companies that base their decisions on data have a return on investment (ROI) that is 30 % higher than that of their competitors. These astounding numbers show what data-driven decision-making is and how much data-driven decision-making can transform the success of a business.

In this post, we’ll go into the best decision-making strategies and resources for maximizing a company’s data resources so that it may make accurate decisions and do incredible things with its BI efforts.

What is data-driven decision-making? 

“Data-driven decision-making” refers to a methodology in which relevant and trustworthy data is used to inform and maintain company decision-making.

Insights, trends, and decisions come from data analysis and interpretation, which drives company plans and actions. Decisions are not based on assumption or personal preference under this approach; actual data confirm them.

Why data-driven decision-making matters

In today’s business world, making data-based decisions is highly important. Leveraging data for decision-making is not advantageous in today’s data-driven environment; it’s necessary for long-term development and success.

  • Data-driven insights.
  • Reduces gut-based judgment risks.
  • Identifies trends and patterns for commercial decision-making.
  • Helps decision-making.
  • Finds novel opportunities and provides companies with an advantage.
  • Improves client awareness and customization.
  • Enables proactive decision-making.
  • Allows businesses to evaluate decisions.
  • Supports making decisions based on facts for better results.
  • Improves organizational productivity.

Quality Matters: Tackling Problems with Data Management and Quality

Businesses need help to gain insights from their massive data sets. Due to data abundance, fragmentation, quality issues, and analytical bottlenecks, organizations need help to gain insights that drive decision-making and business success. 

Use intelligent technologies and effective data management and analysis to overcome such problems. If a company overcomes the dilemma of too much data and not enough insights, it may unlock its data’s full potential and make data-driven choices.

  • Companies are generating massive volumes of data, including information from customers, transactions, sensors, and social media.
  • Problems arise when attempting to make insights from the massive amounts of data available.
  • It is difficult to gain a bird’s-eye view of data due to its fragmentation and silos inside many systems and departments.
  • Poor data quality and dependability result from poor data governance and management policies.
  • Needs to improve in gaining valuable insights due to ineffective data processing and analysis methods.
  • Insufficient personnel and time to manage and analyze extensive data collection.
  • Lack of capacity to identify trends, correlations, and other meaningful data patterns that might benefit decision-making.
  • Challenges in turning raw data into insights that can be used to make business decisions and support growth.
  • The possibility that judgments may be made with insufficient details, leading to undesirable results.
  • Methods and resources are required to efficiently process, analyze, and display data to reach valuable insights.

Decision-Making Strategies Based On Data

Data-driven decision-making in business intelligence helps companies to use data to make successful decisions. These decision-making strategies can help businesses improve data-driven by setting clear goals and using modern analytics data-driven marketing tools.

1. Set Goals and KPIs

  • Spell out the long-term aims that support the company’s overall strategy.
  • Determine the metrics that will serve as checkpoints along the way to success.
  • Goals and key performance indicators should be SMART, specific, measurable, attainable, relevant, and punctual.

2. Get All the Important Information Together

  • Determine the information requirements for making choices consistent with the goals.
  • Set up procedures and guidelines for data collecting to ensure you have all the information you need.
  • You should use decision-making strategies to ensure your data is correct, consistent, and of high quality.

3. Examine Information for Valuable Insights

  • Methods of data analysis include descriptive, diagnostic, predictive, and prescriptive analytics should be used.
  • Use data visualization tools to successfully express your findings and promote awareness.
  • Find the trends and relationships in the data to reach conclusions and provide suggestions.

4. Promote an Environment Where Decisions Are Based on Facts and Data

  • Train staff on data-driven decision-making.
  • Spread the idea that choices should be based on evidence rather than gut feelings or discrimination throughout the company.
  • Integrate data analysis into decision-making through the implementation of procedures and workflows.

5.  Keep Information Safe, Reliable, and Accurate

  • Put in place mechanisms for data quality control to ensure that your data remains accurate and undamaged.
  • It’s essential to regularly check and evaluate data sources to make sure they’re accurate.
  • Protect sensitive data with high-quality data-driven solutions while meeting regulatory requirements.
  • By implementing these methods, businesses may use the potential of data-driven decision-making to improve operational efficiency, increase profitability, and expand market share.

Many data-driven decision-making examples show how data-driven decision-making may be utilized across various disciplines to increase efficiency, improve results, and gain an edge over competitors.

By putting the power of data to work for them, companies may get access to valuable insights and make well-informed decisions, which lead to improved business outcomes.

10 Data-driven decision-making examples are below.

  • E-commerce
  • Energy Management
  • Manufacturing
  • Travel and Tourism
  • Education
  • Retail
  • Healthcare
  • Financial Services
  • Marketing
  • Supply Chain

1. E-commerce

Online retailers enhance website performance through data-driven decisions. They evaluate website traffic, user behavior, and conversion rates to optimize page load speeds, checkout process efficiency, and user experience. This boosts client happiness, revenue, and growth.

2. Energy Management

Data-driven decision-making optimizes energy use and lowers costs for energy companies. Energy-saving options, optimized consumption patterns, and energy-efficient technology are identified by analyzing real-time energy data, weather trends, and equipment performance. This reduces energy waste, operational costs, and sustainability.

3. Manufacturing

Data-driven decision-making improves operational efficiency and quality control. They examine production data, equipment performance, and defect rates to find bottlenecks, optimize production schedules, and enhance quality. This streamlines manufacturing reduces waste, and ensures high-quality goods.

4. Travel & tourism

Data-driven decision-making personalizes client travel experiences. They create trip packages based on customer preferences, travel trends, and feedback data. Satisfaction and loyalty increase.

5. Education

Data-driven decision-making improves student achievement in schools. They identify at-risk kids, administer targeted interventions, and assess student progress by analyzing academic, attendance, and demographic data. This aids program customization, resource allocation, and student achievement.

6. Retail

Data-driven inventory management optimizes retail. They estimate demand, optimize stock levels, and avoid stockouts by analyzing sales, customer demand, and supply chain data. This improves customer happiness, lowers expenses, and maximizes profits.

7. Healthcare

Data-driven decision-making improves patient care. They use patient health information, medical research, and treatment outcomes to uncover best practices, enhance diagnosis, and tailor treatment programs. This improves patient care and quality.

8. Financial Services

Financial institutions use data to analyze creditworthiness and risk. They use credit scores, financial data, and market trends to assess lending, investing, and risk. This reduces chances, improves financial performance, and ensures compliance.

9. Marketing

Data-driven marketing agencies optimize campaigns. Target audiences, messages, and marketing budgets are identified by analyzing consumer behavior data, market research, and campaign performance indicators. This boosts campaign ROI, consumer engagement, and business growth.

10. Logistics

Data-driven decision-making optimizes supply chain operations. They optimize warehouse sites, delivery routes, and supply chain efficiency by analyzing inventory, transportation, and demand data. This reduces prices, lead times, and supply chain performance.

Tools for Data-Driven Decision-Making Business Intelligence

Using Data-Driven Decision Making tools, businesses can speed up their data-driven decision-making processes, get more in-depth insights, and make better-informed decisions that boost growth, improve operational efficiency, and better customer experiences.

By leveraging data analysis and advanced algorithms, these tools assist in generating accurate forecasts and guiding strategic decision-making processes. With their ability to uncover patterns and trends, decision-making tools enable businesses to stay ahead of the competition and drive success in a rapidly changing market.

The unique demands and goals of the company, as well as the complexity of the data and analytics requirements, are all factors that should be considered when selecting the appropriate technologies or using data-driven marketing tools. 

1. Business Intelligence Platforms

Thanks to software applications, Businesses can now collect, analyze, and display data from a broad range of sources. This provides a way for businesses to make informed decisions based on real-time information, which produces a win-win situation for everyone involved.

2. Data Visualization Tools

Many data-driven marketing tools assist in turning complex data sets into visually attractive charts, graphs, and interactive dashboards, which makes it simpler to analyze and communicate results. These tools may be found on GitHub.

3. Software for Statistical Analysis

Programs such as SPSS SAS give sophisticated capabilities for statistical analysis. These capabilities allow companies to discover hidden patterns, trends, and correlations within their data.

4. Data Mining Tools

These kinds of tools assist businesses in collecting valuable information and patterns from large datasets, which may be time-consuming to do manually. Because of this, companies can base their judgments and estimations on the data rather than on their emotional responses alone.

5. Tools for Predictive Analytics 

Organizations can employ machine learning algorithms and predictive models with the help of tools such as IBM Watson Analytics, Microsoft Azure ML, and Google Cloud AutoML. Decision-making tools empower organizations to gain valuable insights, predict trends, and make informed choices.

6. Tools for Data Warehousing

Platforms like Amazon Redshift, Google BigQuery, and Snowflake offer scalable and efficient storage solutions for significant data. These data-driven solutions facilitate data-driven decision-making tools by assuring instant access to information important to the choice.

7. Machine Learning Platforms

Machine learning platforms provide organizations with the decision-making tools and frameworks to design and implement machine learning models. This creates a way for data-driven decision-making through the use of predictive analytics.

8. Cooperation and Communication Tools

Tools like Google Docs, Microsoft Teams, and Slack encourage unity among team members by allowing them to exchange ideas, discuss findings, and mutually make decisions based on available information.

9. Information Security Tools 

These types of decision-making tools, such as Collibra, Informatica, and Talend, are examples of data governance and information security tools. These decision-making tools aid in controlling data quality, guaranteeing data integrity, and implementing security measures to secure sensitive information.

Wrapping It Up

Using data analytics to get business insights and make decisions is a growing industry, as evidenced by several studies. By 2023, more than a third of major companies will employ analysts engaged in decision intelligence. Because of its focus on delivering valuable, explorable data insights, business intelligence solutions are an integral part of business analytics.

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A look into Snowflake Data Types

A look into Snowflake Data Types

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


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.