What Is A Data Driven Culture


10 Steps to Creating a Data-Driven Culture

The establishment of a strong, data-driven culture is still difficult for many organizations, and data is rarely used as the sole foundation for decision making. What is it about it that is so difficult? Our research in a variety of sectors has revealed that the most significant barriers to establishing data-driven firms are not technological in nature, but cultural in nature. We’ve compiled 10 data commandments to assist in the creation and maintenance of a culture that places data at its core: A data-driven culture begins at the top of the organization; choose metrics with care – and cunning; don’t pigeonhole your data scientists within silos; fix basic data access issues quickly; quantify uncertainty; make proofs of concept simple and robust; provide specialized training where needed; use analytics to help employees as well as customers; be willing to trade flexibility in programming languages for consistency in the short-term; and get in the habit of explaining analytical choices.

Massive amounts of data have the potential to catalyze a new age of fact-based innovation in organizations, allowing for the validation of innovative ideas based on solid facts.

While a strong data-driven culture is becoming increasingly common in many organizations, data is rarely used as the sole foundation for decision-making.

Our research in a variety of sectors has revealed that the most significant barriers to establishing data-driven firms are not technological in nature, but cultural in nature.

  1. However, it is significantly more difficult to make this normal, if not routine for employees, which represents an enormous mental change that is difficult to do without help.
  2. 1.
  3. Companies with strong data-driven cultures tend to have top managers who create the expectation that choices must be based on data — that this is typical, rather than innovative or extraordinary — and that this is the rule rather than the exception.
  4. For example, at one retail bank, C-suite executives work together to sort through the information from controlled market trials before making product launch decisions.

As a result, employees who want to be taken seriously must speak with top executives on their terms and in their own language, which causes these habits to spread downward. The example set by a few individuals at the top of a firm may trigger significant alterations in company-wide standards.

Insight Center

2. Make your metric selections with care — and cleverness. Leaders may have a significant impact on employee behavior by carefully selecting the metrics they want to evaluate and the metrics they want people to utilize. Consider the possibility that a corporation might profit by forecasting the pricing fluctuations of competitors. Well, there is a gauge for that: the prediction accuracy over a given period of time. As a result, a team should make specific forecasts regarding the amount and direction of such shifts on a continual basis.

  • For example, a renowned telecommunications company wanted to make certain that its network gave the greatest possible user experience to its most important consumers.
  • By compiling specific measurements on consumers’ experiences, the operator might conduct a quantitative examination of the impact of network enhancements on the general consumer population.
  • 3.
  • Data scientists are frequently segregated inside a corporation, with the consequence that they and business leaders have limited knowledge of one another’s work and interests.
  • Those who have been successful in addressing this obstacle have often done so in two ways: first, by recognizing the problem and second, by addressing it.
  • Several employees of a prominent global insurer are rotated out of centers of excellence and into line positions, where they scale up a proof of concept.
  • Several new jobs in various functional areas and lines of business have been created by a global commodities trading corporation in order to increase the analytical sophistication of the organization; these roles have dotted-line links with centers of excellence.

Companies on the cutting edge have a different strategy.

Senior executives are not required to reincarnate as machine-learning engineers.


The most prevalent problem we hear is that employees working in various sections of a company are having difficulty obtaining even the most basic information.

With limited access to information, analysts perform little in the way of analysis, making it hard for a data-driven culture to take root, much alone thrive.

As opposed to undertaking massive, time-consuming projects to restructure all of their data at once, they provide universal access to only a few essential measurements at a time.

In this case, the data included core loan information such as loan terms, balances, and property information; marketing channel information on how loans were created; and data that reflected the customer’s overall banking relationship.

Demanding that additional numbers be linked to this data source in the future can significantly increase the likelihood of its adoption.

Identify and quantify uncertainty.

Despite this, most managers continue to rely on their employees for solutions without a comparable level of trust in their own abilities.

The requirement for teams to be transparent and quantitative about their degrees of uncertainty has three significant consequences.

Is there a dearth of instances to create a credible model?

One retailer discovered that the apparent decline in redemption rates from their direct marketing programs was driven by address data that was becoming progressively out of date.

Second, when analysts are required to systematically analyze uncertainty in their models, they get a better knowledge of their models.

insurer have failed to appropriately respond to changing market conditions.

As a consequence, it was able to prevent costs caused by unexpected surges in claims.

It was reportedly said by the chief merchant of a shop that “test and learn” was actually just a code word for “tinker and hope.” At his company, a team of quantitative analysts worked in collaboration with category managers to undertake statistically rigorous, controlled trials of their ideas before implementing them across the company.

In the field of analytics, promising concepts outweigh practical ones by a wide margin.

After holding an internal hackathon and crowning its winner — an elegant upgrade to an online procedure — one big insurer decided to discard the concept since it appeared to need costly adjustments to underlying infrastructure.

A preferable method is to construct proofs of concept in which the feasibility of the notion in production is a central component of the proof of concept.

As an example, in order to implement new risk models on a large-scale, distributed computing system, a data products company began by implementing an extremely basic process that worked end-to-end: a small dataset flowed correctly from source systems, through a simple model, and was then transferred to end users.

  1. 7.
  2. Many businesses engage in “big bang” training initiatives, only to find that workers quickly forget what they’ve learned if they don’t put it to use straight soon after they’ve finished.
  3. One retailer waited until just before the start of the first market trial before training its support analysts in the intricacies of experimental design and methodology.
  4. 8.
  5. It’s easy to lose sight of the potential role that data fluency might play in increasing employee happiness.
  6. Unless the concept of learning new skills to better handle data is presented in a compelling manner, few employees will be motivated enough to persevere and make changes to their work practices.
  7. It wasn’t long ago that the analytics team at a prominent insurer learned itself the foundations of cloud computing so that they could experiment with new models on enormous datasets without having to wait for the IT department to catch up with their requirements.

When it came time to sketch out the platform requirements for advanced analytics, the team was able to provide more than just a description of a possible solution.

9) Be prepared to give up some flexibility in exchange for more consistency – at least in the near run.

Each organization may have its own preferred sources of information, customized metrics, and preferred programming languages to use.

It is possible for companies to squander endless hours attempting to reconcile marginally different versions of a measure that should be universal in nature.

The use of different coding standards and programming languages within an organization means that every move by analytical talent requires retraining, making it difficult for them to move around.

In lieu of this, businesses should choose canonical measurements and programming languages.


A single, proper method to the majority of analytical issues is almost never found.

As a result, it’s a good idea to ask teams how they handled a challenge, what options they evaluated, what they believed the tradeoffs to be, and why they picked one technique over another when evaluating their work.

One multinational financial services organization initially believed that a fairly basic machine-learning model for detecting fraud would be unable to run rapidly enough to be deployed in production.

However, was later discovered that by making a few minor changes to the model, it could be made to run extremely quickly.

Companies — and the divisions and individuals that make up those companies — frequently rely on old habits because the alternatives appear to be too risky.

The mere desire to be data-driven, on the other hand, is insufficient.

Companies must create environments in which data-driven decision-making can flourish if they are to be successful. Leading by example, establishing new habits, and setting expectations for what it means to truly base choices on data are all effective ways for leaders to encourage this transition.

What is Data-driven Culture and how to develop it?

Data may be found everywhere. It can take the shape of figures, spreadsheets, photos, movies, and a variety of other formats and media. Companies are now utilizing and capitalizing on data in order to make an impact and develop. Data is really crucial today. And, in order for enterprises to thrive and grow, a data-driven culture is vital to success. However, what exactly is data-driven culture? Put another way, it is the process of using data to make choices at all levels of a company. A data-driven culture is characterized by the replacement of gut instinct with facts and assumptions while making judgments.

Organizations must take use of data in order to get insights and make decisions based on that data.

What goes into building a data-driven culture?

When workers are clear on the driving metrics they are accountable for and how those metrics affect the Key Performance Indicators, or KPIs, they are said to have a data-driven culture in their organization, according to the term. Data democratization is required, which means that the information must be made available to the common user. The organization requires that its workers comprehend and use data in order to make decisions that are appropriate for their positions. It requires citizen analysts, who are capable of doing basic analyses and are not reliant on the data team for their work.

  • It is necessary to have data governance and Master Data Management in place in order to preserve the consistency, correctness, usability, and security of the information.
  • Despite the fact that they appear to be complicated, they are not.
  • It is crucial to emphasize, however, that data culture is not a one-time effort that can be put on hold after it has been accomplished.
  • Each of the four components of a data-driven culture is described below: Data Maturity, Data Driven Leadership, Data Literacy, and the Decision-Making Process.
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Also read: A CDO’s Guide to Building a Culture of Data

A strong data maturity foundation is essential for establishing a data culture. It is concerned with the raw material, which is data, as well as with its administration. An organization with strong data maturity has high standard data quality, and there are checks in place to ensure that it continues to be so for the foreseeable future. Metadata management must be in place, and it must be matched with the key performance indicators (KPIs) in order to achieve a high degree of data maturity. Additionally, it is vital to document the data lineage of a data set, which aids in understanding what has transpired to it since its inception.

Data maturity is also affected by aspects such as usability, ease of access, and the availability of a scalable and agile infrastructure.

Consequently, data that is not easily available will not be used by the company in such circumstances. Furthermore, if the KPIs are not aligned, firms would spend the majority of their time verifying and establishing alignment rather than focusing on the effect of their actions.


To have a strong data culture, you must first have solid data maturity. There are two parts to this process: managing the raw material (data) and managing the finished product (product). The data maturity of an organization is characterized by high standard data quality, which is maintained via the use of checks. Metadata management should be in place and should be matched with key performance indicators (KPIs) in order to achieve a high degree of data maturity. In a similar vein, it is vital to document Data Lineage, which aids in understanding what has happened to it since its inception.

Usability, simplicity of access, and a scalable and adaptable infrastructure are all variables that influence data maturity.

Consequently, data that is not immediately available will not be used by the company in such situations.

Data Literacy

Data literacy is defined as the capacity to read, use, assimilate, and comprehend data in order to come to a meaningful conclusion or conversation. When it comes to an organization, data literacy does not always imply that personnel have a thorough grasp of how to use and analyze data. It is necessary for everyone to have a certain level of data literacy, which varies based on their work function and the decisions they are responsible for making. However, it also asks for ensuring that there are no data skeptics in the group.

For example, Susan, a product manager, is a citizen analyst who wants to utilize data to effect change, but her manager is a data skeptic who opposes her efforts.

Decision-making Process

To gain the most value out of data, it needs to be integrated into the decision-making process from the beginning. Is there a planning framework in place to pick amongst projects to work on, or is there a mechanism in place to assess the decisions that have been made thus far? As an example, if a marketing budget is allocated in accordance with the anticipated return on investment, it may be argued that data is being utilized to make decisions. The vast majority of firms do not have a structured, data-driven decision-making process, as described above.

How to create a data-driven culture

Conversations about data-driven organizations frequently center on tools, big data, and technical breakthroughs that have made storing, processing, and analyzing data faster and more cost-effective in recent years, among other things. However, establishing a data-driven culture throughout the organization is critical to moving beyond a few successful data initiatives and islands of excellence that are exclusive to specific business functions. A data-driven culture is one that encourages the use of data in decision-making processes.

It is concerned with the collection, cleansing, and curation of valuable data from across the organization.

When it comes to distinguishing the business through artificial intelligence (AI) and machine learning, it understands the need of a robust data foundation (ML).

Data literacy is high in this culture, and there is a strong understanding that data can be used to improve the performance of all employees. What strategies can executives use to foster a data-driven culture?

Walk the Talk

Executive support is required, but it is not sufficient, in order to establish a data-driven culture. The C-suite must go above and above in their assistance. They must remain engaged and involved throughout the process, clearly integrating data with sound business judgment in order to make judgments. This involves aggressively purging the binders full of unused reports, spreadsheets, and dashboards that were prepared just to gratify them and provide the appearance of a data-driven culture to the outside world.

  1. Aside from that, executives should aggressively seek evidence that may be in opposition to their existing convictions.
  2. Data is empowering, but it may also elicit strong emotional responses from the user.
  3. This can be a little unpleasant at times.
  4. They are concerned about losing their relevance or influence over the story.

Organize for Success

Each data endeavor should be managed as if it were a standalone product, rather than as a result of another initiative. This entails putting in place the appropriate organizational structure to facilitate it. In his eBook, Becoming a Data-Driven Organization, my colleague Joe Chung discusses the need of establishing an Analytics Center of Excellence. At Amazon, we refer to this as a single-threaded leader, who is completely empowered and whose sole responsibility is to ensure that one specific goal is met on a daily basis.

Make sure there is an empowered leader who is waking up every morning and caring about only that one issue if you want to establish a data-driven culture.

Irrespective of the reporting structure, information technology (IT) should play a significant role that goes beyond just developing technological capabilities to support data projects.

Prior to becoming CTO, I was the team leader for the company’s worldwide products, applications, and data teams.

Create a system that is completely accountable while still being inclusive in order to avoid the creation of further barriers.

Treat Data as an Organizational Asset, Not a Departmental Property

It is preferable to handle each data venture as a standalone product rather than as an afterthought or result of another project. To do so, the appropriate organizational structure must be put in place. In his eBookBecoming a Data-Driven Organization, my colleague Joe Chung discusses the need of establishing an Analytics Center of Excellence. At Amazon, we refer to this as a single-threaded leader, who is completely empowered and whose sole responsibility is to ensure that one specific goal is met on a continuous basis.

With the increasing separation of data engineering and analytics from IT, there is an increased likelihood of conflict.

When it comes to end-to-end business cycles, cross-departmental processes, and transactional systems, IT is in a unique position to provide a comprehensive picture that yields a wealth of relevant information.

It was possible for my team to not only build a superior data analytics platform that broke down silos, but also to solve holes in transactional systems in order to gather and utilize data more effectively since we had end-to-end ownership.

Democratize the Data

A data-driven culture entails more than just relying on data to make important choices. A data-driven culture enables and empowers frontline staff to make a large number of modest, everyday decisions based on the data. In the digital economy, time is of the essence, and leveraging data to swiftly test product ideas, design decisions, and hypotheses may help businesses become more responsive and flexible. High-frequency firms that offer economic value usually transition away from decision-making based on HiPPO (highest paid person’s opinion) to a decentralized data-driven decision-making process.

Epic leverages AWS to obtain an up-to-the-minute insight of gamers’ happiness and involvement, and then they make this information available to their game creators to help them create better games.

This results in a more enjoyable gaming experience for the players as well as a more engaged user community.

Speak the Same Language

Language has played an important part in the establishment and maintenance of civilizations from the beginning of time. A shared language aids in the communication of the values, beliefs, and ideas that shape a culture’s identity. A data-driven culture is no different from any other. In order to foster a data-driven culture, businesses must establish a consistent vocabulary for talking about data. This begins with the establishment of essential business measures that are understood throughout the organization, but extends beyond that to include the identification of factors that feed into those metrics.

  1. Consistency may be achieved by establishing a limited number of critical outcomes for which the entire organization is held accountable and has visibility.
  2. Do not merely measure what you can; instead, measure what you should be able to measure.
  3. Once you’ve determined what they are, come up with a consistent definition and make certain that everyone knows it.
  4. For the most part, a data-driven culture flourishes when senior leadership is involved, middle management is empowered, frontline people are motivated, and organizational silos are eliminated.
  5. In order to differentiate themselves in the marketplace and to unite their employees, successful firms must implement a data-driven culture at scale in their enterprises.
  6. @ishitv Read on to find out how you can use data to change your business.

The Importance and Advantages of a Data-Driven Culture

A data-driven culture is characterized by the ability to make decisions based on statistics crunched and insights developed via data analysis. As Gartner’s Vice President Analyst, Alan Duncan explains, “The benefits of a data-driven culture include the ability to study and organize data with the purpose of better serving an organization’s clients and consumers.” It also helps to strengthen and accelerate the decision-making processes of a company.

Photo Credit:Unsplash

Data is being collected by the vast majority of enterprises, yet data on its own has no significant value. Taking advantage of acquired data needs working with the stored data, but the problem-solving iterations must not be limited to a few use cases at a time.

Storing vast volumes of data does not always bring any benefits, and as a result, it is necessary to shift away from big data and toward addressing business challenges through the adoption of a data-driven approach.

Helps Make Efficient Decisions:

  • Data is being collected by the vast majority of organizations, yet data on its own is of little use. It is necessary to engage with obtained data in order to maximize its value, but problem-solving iterations should not be limited to a few use cases. Large volumes of data are not always beneficial, and as a result, it is necessary to shift away from big data and toward solving business challenges through the adoption of a data-driven approach.

Supports Progress Tracking:

  • An organization’s data-driven culture assists them in moving away from the use of data for only report development and progress tracking. Developing transparent reports to better understand the success of a firm, for example, a 360-degree picture of the organization, is assisted by this technology.

Increases Coordination:

  1. A data-driven culture may enable the automation of routine processes and the coordination of marketing, sales, and operational initiatives across organizations. Improvements in communication and coordination across departments and teams result in increased consistency in goods, services, and procedures. These improvements may be analyzed to produce a better and more exact understanding of the future demands of the market and target audience.

A data-driven culture serves as a fertile foundation for the democratization of information. Due to the lack of gatekeepers, anybody who has access to the data might claim ownership of the information. Data democratization directly enables departments to utilize past customer data to better understand customer demands without having to speak with the customers themselves first. Having the capacity for a company to recognize prospective possibilities based on figures crunched from data is what the future holds and, when utilized appropriately, may be a recipe for success.

Data-Driven Culture – A Key To Success

Data is now regarded to be a critical component of every organization’s operations. Information gleaned from the data is a valuable source for enhancing company operations and decision-making, as well as for anticipating future trends and behavior and creating new revenue-generating opportunities. A data-driven culture cannot just follow the figures crunched by the data; rather, it must encourage cooperation amongst divisions within the firm, as described above. It is necessary for entities within the business to leverage insights generated through data analytics as a platform for collaboration.

According to our observations, the attitude of a company is the most significant obstacle to the establishment of a data-driven culture in the firm.

How Nisum Can Help

At Nisum, our key objective for 2020 and beyond is to establish a data-driven strategy so that businesses may transform their business landscape by utilizing the three pillars of Digital, Business Agility, and Insights and Analytics to alter their business environment. A data-driven culture is enabled when firms are able to improve their digital capabilities while also automating functionality in order to establish a performance-oriented, productive workplace. To find out more about our services, please get in touch with us.

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Written by

Nisum has been working with Vyasraj Vaidya as a principal data scientist for the past year. He holds a Master’s degree in information technology from the Technical University of Ilmenau in Germany, and he has previous job experience in the media and entertainment industries.

Data Culture

It is the collective behaviors and attitudes held by individuals who respect, practice, and support the use of data to better decision-making that constitute a Data Culture. An organization’s operations, attitude, and identity are all influenced as a result of the integration of data. Every employee in your organization will benefit from a Data Culture, which will enable them to be really data-driven in their approach to addressing your most difficult business issues.

Practice data-driven behaviors

Align data and analytics with the goals of the organization.

Value strategic data use

Prioritize data in decision-making and corporate processes to ensure that it is used appropriately.

Encourage sharingcommunity

Join together in support of a common objective to lead through data.

Invest in Data Culture and technology to become a data-driven organization

Organizations are investing trillions of dollars to become more data-driven, yet only 8 percent of them are effective in scaling analytics to extract value from their data. To transform into a data-driven business, investments in Data Culture as well as technology are required to change the way people make choices. People who are empowered by Facts Culture and the appropriate technology may ask questions, debate ideas, and make judgments based on data, rather than intuition, rather to merely intuition.



How Data Culture fuels business value

According to research from IDC 2, firms that have a Data Culture are more likely to realize the full potential of their data. You can learn more about how culture contributes to the success of data-leading organizations—those with strong Data Cultures—by reading the IDC whitepaper. Examine the habits of data-savvy CEOs, as well as IDC’s advice for establishing a strong Data Culture. The following are the data-leading organizations that were surveyed:

of CEOs want a data-driven organization


“Tableau was the catalyst to help employees have that ‘aha data moment.’ That flicker of data cognition turning into deeper understanding was what Nissan needed for success in a digital world.”

Z Holdings, the parent company of Yahoo Japan, established a Data Culture based on leadership commitment and a well defined data strategy to help the firm succeed. Now is the time to watch With the use of contact center analytics, Verizon is able to cut support calls by 43 percent while also lowering operating expenses. More information may be found here. Nissan created a corporate Data Culture that has fueled innovation and resulted in multi-million dollar savings. Find out more.

Start building a Data Culture with these resources

The Data Culture Playbook is designed for business executives who want to create a robust, data-driven company from the ground up. You will learn how to establish the fundamental behaviors and attitudes of a Data Culture, beginning with four main emphasis areas:

  • Improve the alignment of leadership metrics with corporate goals
  • Provide data sources to address crucial decision points
  • Increase the value of a product through specialized use cases. Encourage the finding of large amounts of data.

Take a look at the playbook

Learn about the impact of data literacy

Data literacy (the capacity to investigate, interpret, and communicate with data) contributes to the development of a strong Data Culture and the success of individuals and organizations. Organizations that prioritize and invest in data literacy are able to close the analytics skills gap, foster more cooperation, gain competitive advantages, and more. Learn how to get started, where to receive professional help, and where to discover important resources.LEARN MORE

Blog: Build a resilient future with these data-driven habits

Learn how to create a Data Culture in order to construct a resiliency foundation and make an investment in the future. Now is the time to read

Webinar: How to run, build, and expand your Data Culture

The Data Culture Playbook teaches you how to generate greater value by maturing data usage across your whole business. Now is the time to watch

Blog: How to nurture a healthy Data Culture in 3 steps

Even though every firm is a data company, modern organizations build Data Cultures to differentiate themselves. Read on to learn three methods to get started creating your Data Culture. Now is the time to read

“When we first started with Tableau, we were just thinking about dashboarding and reporting. We never thought Tableau would fundamentally change the DNA of the organization.”

INDIA’S LENOVO INDIA is led by ASHISH BRAGANZA, the company’s Director of Global Business Intelligence.

Read the story

It is only Tableau that brings together an unwavering emphasis on how people perceive and interpret data with the powerful, scalable infrastructure required to manage the world’s most complex enterprises.

Methodology to build a data-driven org

Tableau Blueprint provides you with curated best practices and prescriptive direction to help you build the capabilities you need to transform your business into a data-driven organization.

Unparalleled community

The Data Leadership Collaborative brings together executives who share a common interest in building data-driven enterprises. Learn new things, make new friends, and expand your network.

3 ways to build a data-driven culture in your organization

According to Samir Boualla, chief data officer of ING Bank France, “I believe that one of the most significant impediments is that data is very frequently associated with or viewed as something which is entirely technical.” “The most difficult problem is to explain and provide instances that move data from a purely technological standpoint to a purely commercial perspective,” says the author. Boualla likes to draw the analogy of weather predictions, which we all rely on to help us arrange our daily activities.

In other words, “it’s not simply an output from a system; it’s something I can utilize in my daily decision-making,” says the author.

“I would like to see our data experts be able to communicate in a variety of languages,” she stated.

“I urge them to think of data governance as a collection of activities or business practices that might help them manage their data more effectively and efficiently.” “That’s all there is to it.”

Show how data solves problems at all levels of the organization

Elizabeth Puchek, the second Chief Data Officer of the United States Citizenship and Immigration Services, gained trust by conducting listening tours throughout the business “to figure out how to relate the data to the purpose,” as she put it. A data-transfer procedure was found by Puchek’s team that could be significantly improved, reducing the time required from hours per day to minutes, earning credibility with personnel in the field. Her team gained the attention of corporate leaders after they successfully resolved a high-level problem involving a backlog of cases.

“Before now, leadership had never had that enterprise vision,” she explained.

“One of our data-driven executives distributed access to the dashboard to every single adjudicator so that they could monitor their own performance on a regular basis.

The Chief Data Officer (CDO) is “the glue that holds everything together and develops the bridges necessary to comprehend and manage data difficulties,” according to Audet. “The ability to communicate effectively is essential for success.”

The Four Key Pillars To Fostering A Data-Driven Culture

Most firms today are attempting to capitalize on the potential value that may be realized by becoming more data-driven. Researchers at the Massachusetts Institute of Technology (MIT) showed that organizations that use data-driven decision making had higher production and productivity by 5 to 6 percent than their competitors. According to Richard Joyce, Senior Analyst at Forrester, “For a typical Fortune 1000 organization, a ten percent improvement in data accessibility will result in an increase in net income of more than $65 million.” According to Forrester Research, data-driven firms are increasing at an average rate of more than 30 percent each year on average.

  • Recent research from NewVantage Partners revealed that firms are failing in their efforts to become more data-driven.
  • In addition, the percentage of respondents who stated that they had “built a data-driven organization” declined to 31 percent in 2019 from 32.4 percent in 2018 and 37.1 percent in 2017, according to the survey.
  • As opposed to seeing increasing digital transformation, we’re witnessing a significant amount of digital retreat, which is disheartening.
  • The majority of individuals nowadays will be quite upset if they are without their phones for a lengthy amount of time.
  • What would happen if your team didn’t have access to your data for a whole day, in a similar vein?
  • If you were to assign a rating to your organization’s response, where would it sit on the following scale, and why?
  • Not that you do not have data or that you do not utilize it on a regular basis; rather, it indicates that data is not crucial to how you do business.
  • It is only when the loss of access to your data becomes disruptive or uncomfortable that it has become a vital element of your regular business operations.
  • A data-driven culture cannot be purchased or created, despite the claims of marketing materials to the contrary.
  • This may come as a letdown to many CEOs who had thought that technological advancements would allow them to catch up with their more data-savvy rivals.
  • According to the aforementioned survey, the most significant obstacles to becoming data-driven were people (62.5 percent) and procedure (30.0 percent), rather than technology (only 7.5 percent ).

If you want to create a data-centric atmosphere where data is continually relied on to improve efficiency and effectiveness, you’ll want to concentrate on the four pillars listed below: To embrace data, you must first transform your organization’s collective thinking, which can be one of the most difficult tasks to overcome.

As you seek to lead your team in a new path, you’ll need to be both thorough and patient. The following areas of emphasis can assist with shifting one’s frame of mind:

  • Executive sponsorship: If your executives don’t believe in the power of data, it will be very difficult to convince people to adopt a data-driven approach in their organization. Establishing a data-driven culture via leadership is one of the most effective strategies of accomplishing this goal. Fast wins: Generating quick wins is one of the most effective change management strategies. People find it more difficult to deny the need for change when they see and feel the practical advantages of data-driven decisions. With each new victory, your data-driven momentum will continue to build. Testing and learning through experimentation (TestLearn): Companies that are data-driven, such as Amazon, are not afraid to try ideas, make errors, and iterate. You establish the discipline of depending on data to support decision-making and help you develop more rapidly when you test everything

(2) Enhance the SKILLSET of the workforce Your staff will require specialized data-related expertise and abilities in order to be successful with data. While you can undoubtedly acquire people with data capabilities, your present employees have important subject knowledge and experience that can’t be obtained through hiring. The following areas of emphasis can assist you in improving your staff’ ability to work with data:

  • Data literacy: The finest library in the world is worthless to someone who is unable to read or write. You must guarantee that your staff receive fundamental training on how to read and interpret the data they will be expected to consume and use on a regular basis. Data storytelling: People need to be able to convey crucial insights they discover in data to others, which goes beyond simply being data literate. The ability to blend data, story, and graphics effectively will be required to guarantee that insights are simply comprehended and can be used to drive action. Availability of data analysts: Some firms have struggled to recruit a sufficient number of data analysts. In their current state, they lack data trainers and coaches who can assist the rest of their coworkers in becoming more data literate.

3. Maintain the TOOLSET’s sharpness. Organizations can amass a diverse collection of data systems and tools over the course of their existence. Unfortunately, the development of a data-driven culture is frequently hampered rather than aided by this fragmented environment. If you concentrate on the following areas, you can ensure that your technology foundation is strong enough to support the establishment of a data-driven culture:

  • Develop a single version of the truth: No matter how many data systems your business has, you must establish a consistent data language. In order for your business to succeed, it will require a single picture of its operating metrics that everyone accepts as the genuine, trustworthy data
  • Model of self-service: It is possible to democratize data so that it is accessible to a wider range of business users. This not only frees up your analysts and data scientists to work on more important initiatives, but it also empowers employees to utilize information more often. Automation: In today’s world, many labor-intensive analytics operations, such as data cleaning and reporting, may be performed automatically. Wherever and whenever it makes sense, you’ll want to offload as much of the data-processing effort as possible to computers, allowing humans to focus on adding value in more productive ways. Integration with current processes or systems: When your analytics tools are integrated with your existing processes or systems, they may become even more powerful. For example, by using dashboards as meeting agendas, you may increase the efficiency of your meetings significantly.
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4. Consolidate the information SET Data is only a means to an end in and of itself. Your organization’s employees’ acceptance of new information will be determined by the relevancy and quality of the data they are presented with. The following areas of attention can help to guarantee that your data is valuable, trustworthy, and protected:

  • Achieving strategy alignment is difficult for many firms because they are unable to effectively identify and express their primary goals. If your data is not intimately related to assessing your company’s success, it will be of little benefit to you. In order for your analytics tools to be effective over time, they must be tightly connected with your company plan. Otherwise, the output (data) will become less and less valuable. Data governance: If you consider data to be a valuable corporate asset, you’ll want to ensure that it is protected and maintained in good condition. However, it is critical to strike a balance between control and accessibility so that the drive for compliance does not outweigh people’s capacity to generate value from the data. Data privacy and security: In order to minimize possible risk, you must guarantee that data privacy is respected and that data is used securely. Business users should be well educated on the ramifications of failing to protect their data in a secure manner. One aspect of developing a data-driven culture is educating everyone on how they may play a part in the protection of these digital assets.

The examples provided under each of the pillars are merely a portion of the elements that contribute to the development of a data-driven culture in an organization. In the hope that they would serve as a useful foundation for examining any possible gaps that you may have in your overall data strategy, the four pillars are as follows: A data-driven culture in your firm will require time and work to establish; it is a long-term endeavor rather than a short-term one. The most challenging component of cultural transformation will continue to be dealing with people on a daily basis.

If you concentrate your efforts on the categories listed above, you should ultimately reach a tipping point where the number of data champions in your business outnumbers the number of data avoiders.

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Data culture – Wikipedia

Data culture is a principle that has been established in the process of social practice in both the public and private sectors. It requires all employees and decision-makers to concentrate on the information conveyed by the existing data and make decisions and changes in accordance with the results, rather than leading the development of the company based on their previous experience in the particular field. General economic and social trends in the market, sales volume of items, and even the performance of employees, as measured by their efficiency and productivity, are examples of the types of information that may be collected.

In general, departments and organizations must allow data to speak for itself and place their faith in the guiding of statistics in order to establish a data culture.

Being a successful data-driven business necessitates the active engagement of all employees who are involved in the organization; therefore, free access to data is critical in the process of becoming one.


Because of its increasing popularity in recent years, the concept of data culture has been garnering attention in the corporate world since the beginning of the twenty-first century. Although the concept was initially proposed in a scientific context, it is now connected with both the scientific field and the social sectors.

  • In his scholarly work on biodiversity, Geoffrey C. Bowker introduced the concept of “local data culture” in the context of biodiversity in the year 2000. As part of a series of announcements made in 2014, Microsoft stated their desire to integrate data culture into everyday life through the use of its services such as Office 365, Azure, and SQL Server. In collaboration with Hortonworks and KPMGUK, Microsoft organized a series of workshops about data culture in 2015. The workshops provided data analysts and other professionals working in the field of Big data with an opportunity to better understand the data culture of the company and to assist them in developing their own data culture in the private sector. The Data Power Conference 2017 took place in Canada on the 22nd and 23rd of June at Carleton University in Ottawa.

Components and roles

Participants are both data providers and those who have the ability to make significant contributions to the data culture by implementing impactful changes. It is expected that personnel at all levels of an organization will get the capacity to illustrate their work with comparative statistics as part of the process of developing a data culture inside that organization. This includes, but is not limited to, the aim of their profession, the purpose of a certain activity, and potential remedies they may provide in response to data-driven challenges they have identified.

Data scientists

Due to the fact that they frequently receive first-hand material and raw data, data analysts play an important role in the establishment of a data culture. The way in which they connect all of the components together can determine the effectiveness of communication between ordinary participants and decision-makers. In addition, they are in charge of doing an analysis of the information provided by the data. It would be ideal for a data-driven organization to have data scientists assigned to each specialized department of the organization, ensuring that data is always available when needed.


Decision-makers in a company are individuals who implement changes and set the direction of the firm’s development. Using the patterns and information shown by the data collected either internally inside their own firms or from statistics of the target market they wish their corporations to target, they would make key business choices in this situation. Additionally, in order to establish a data culture, decision makers must express their plan to need data analysis, therefore increasing the incentive of employees who interact with raw materials.

Data-driven companies

When it comes to driving both significant market choices and their everyday activities, the Microsoftteam, led by Satya Nadella, is heavily reliant on data. Microsoft focuses on data visualization and believes that participants and workers should have the ability to see the firm’s data, according to the corporation. Individual employees are encouraged to become involved and contribute to the firm’s future through the usage of technologies like as Power BI, according to the corporation.


Capita is a British company that helps clients in both government agencies and businesses gain a better understanding of themselves via the use of modern data analytic tools.

They have been in business since 1984 and encourage their clients to develop their own data culture by utilizing relevant databases in their own fields of work.


Socrata is a US-based corporation that provides services to both the public and private sectors, as well as civil society organizations. These organizations help businesses and organizations in obtaining open data from the federal government in order to either enhance the working process of the government or to aid social groups who are in need of assistance. There is a strong connection between their fundamental principle and open data, and they prefer to concentrate on firms that require capital to handle and analyze data.

Data culture

Data culture (datenkultur GmbH) is a German firm that provides business intelligence products and services. It was established in 2003 and has been actively involved in the development of corporate data culture since 2006. Microsoft tools such as SQL Serverdata warehouse and Power BI are used to deliver the technical services, but the business intelligence strategy services are not tied to any one product line. The idea is to assist managing workers or board members in making better and more efficient use of the data that already exists.

Counter opinions

Some businesses believe that it is critical to maintain the confidentiality of information at the executive level. Although it is conceivable for all employees in a firm to work together to create and analyze data, it is not possible for data to be free from the approach of participants at the most fundamental level of abstraction. Some businesses employ the concept of a data warehouse, which is a system that restricts access to information. Only those in control of the data could gain access, and everyone else seeking access would have to go through a screening process with warehouse personnel.


As long as data bureaucracy is convenient for free access toDatabases, a high number of users may cause the processing speed of a specific system to be slowed down. It may also be difficult for users to search for what they are looking for if the data bureaucracy system is not equipped with the necessary tools to assist them. This is an example of when a Data Warehouse might be more efficient for users who are not able to perform professionally when looking for information.


A logical approach to starting or developing a business is diametrically opposed to anempiricalmethods of operation. Individual spirits and pre-existing world cognitions are frequently included in the evaluation of rationalists’ judgments and actions in the world. In this method, decision-makers rely on logic rather than social phenomena and phenomena that arise at the front lines of an industry in order to create changes in their organizations.

The fact that the decision-making process is no longer exclusive to the executive team has caused some people at the management level to be hesitant, and as a result, they have refused to promote a data-driven environment.

See also

  • Data activism, data analysis, data governance, data science, open data, and the Research Data Alliance are all terms that come to mind.


  1. Poornima Ramaswamy and Ramaswamy (June 2015). “How to Develop a Data-Driven Culture” (PDF). Cognizant. Obtainable on November 29, 2017
  2. Kristina Powers and Angela E. Henderson are co-authors of this work (25 May 2018). Building a Data Culture in Higher Education is a big deal these days. It is published by Routledge with the ISBN 978-1-351-69453. Rob Kitchin, Tracey P Lauriault, and Gavin McArdle are co-authors on this paper (8 May 2017). The data, as well as the city. Rob Kitchin, Tracey P. Lauriault, and Gavin McArdle are co-authors of this article. Abingdon, Oxon.ISBN978-1138222632.OCLC992119756
  3. Abingdon, Oxon.ISBN978-1138222632. The five basic components of a data-driven society, according to Carl Anderson. TechCrunch. Obtainable on November 29, 2017
  4. Geoffrey Bowker is a writer who lives in the United Kingdom (2001). “Download Limit Has Been Exceeded.” 30(5): 643–683, CiteSeerX 10.1177/030631200030005001.S2CID220879983
  5. “A data culture for everyone – The Official Microsoft Blog”
  6. “A data culture for everyone – The Official Microsoft Blog” The Official Microsoft Blog, published on April 15th, 2014. “Microsoft Data Culture series for Developers, Data Architects, Data Scientists, and Database Administrators events in Edinburgh, Leeds, Birmingham, Reading, and London,” according to a report published on November 16, 2017. Faculty Connection is a service provided by Microsoft UK. Carleton University’s “Data Power 2017 – Carleton University” was retrieved on November 9, 2017, from carleton.ca. Obtainable on 9 November 2017
  7. 1981–, Tomasz Tunguz is a writer who lives in Poland (26 May 2016). Winning with data means transforming your culture, empowering your people, and influencing the course of history. Frank Bien was born in 1967 and has been living since since. Hoboken, New Jersey.ISBN9781119257394.OCLC951028197.CS1 maint: numeric names: authors list (link)
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  9. Patil, abcDJ, and others (2015). Data-driven: fostering a data-driven culture. Hilary Mason is a writer who lives in the United Kingdom. Sebastopol, CA.ISBN9781491921197.OCLC904285472
  10. Sebastopol, CA.ISBN9781491921197.OCLC904285472 Lynn Torbeck is the author of this work (Autumn 2011). “Culture of data.” Journal of Validation Technology.17(4): 12+ – available online through Academic OneFile
  11. “Using Power BI, you can enable your business to embrace a data-driven culture.” The Microsoft IT Showcase is a showcase of Microsoft technology. 16 November 2017
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  13. Our story”.Capita. Retrieved on the 16th of November, 2017. “About us”.Capita. Retrieved 9 November 2017
  14. Ab”The Data Platform for 21st Century Digital Government”.Socrata, Inc. Retrieved 15 November 2017
  15. Ac”The Data Platform for 21st Century Digital Government”.Socrata, Inc. “What Exactly Is Socrata? Learn everything there is to know about the company “It was retrieved on November 16, 2017 from Socrata, Inc. Mike, and Barlow (2013). Big data has become a way of life. In Sebastopol, California, O’Reilly Media publishes the book with the ISBN 9781491946725 and the OCLC number 87854355.

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