Why Data Driven Culture is Important for You

By Veritis

Why Data driven Culture is Important For You

What is data driven culture? It embraces the use of analytics and data to get corporate insights that result in advancements. It gives firms that are data driven a competitive edge. In short, data driven companies fare better than their counterparts.

IDC projects that by 2025, there will be 175 zettabytes of data worldwide. Our World in Data claims that growing internet and broadband access, mobile phone use, and social media are the main forces behind global data expansion. However, the ability to use data effectively, not the amount, is the most important for the company.

Companies are embracing a data driven approach to stay ahead of their competition and to achieve results after seeing how data trends are upending whole marketplaces. As a result, data driven culture is being embraced by various companies, and these data driven companies “are increasing at an average of more than 30% yearly,” according to a Forrester analysis.

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What is Data Driven Culture?

What is Data Driven Culture

Taking chances typically pays off in business, but businesses should refrain from seizing opportunities at any cost. The data driven culture comes to the rescue at this scene. Data driven strategy approach is a method of utilizing data insights to find new business possibilities, provide better customer service, increase sales, and more. It enables firms to carefully prepare and make decisions based on facts to fulfill their business objectives.

A data driven choice is supported by empirical facts, allowing executives to make wise decisions that favorably impact the company’s bottom line. A decision-making process primarily based on guesswork is the antithesis of one that is data driven. Data driven company executives may consult their instincts when making decisions, but they only take precise measures in response to what the data driven culture pulls up.

A data driven choice is supported by empirical facts, allowing executives to make wise decisions that favorably impact the company’s bottom line. A decision-making process primarily based on guesswork is the antithesis of one that is data driven. Data driven company executives may consult their instincts when making decisions, but they only take precise measures in response to what the data shows.

Business executives in data driven company are aware of the advantages of using data insights to guide their decisions. Based on McKinsey Global Institute research findings, MicroStrategy estimates that data driven organizations are 20 or more times more likely to get new clients and six times more likely to maintain them. However, such executives need to be able to rely on skilled data experts and technological tools that can reveal the value of the data. Data driven culture experts may also offer advice on the most effective methods for gathering, storing, analyzing, and safeguarding critical corporate data.

If the insights from the data driven approach do not disclose anything, the data driven framework is of little to no value in and of itself. The value comes from what is done with the data, not from the data itself. Data analytics, or analyzing data to get business insights, is a valuable tool for firms driven by data. The information may then be used to improve operations or solve problems in the corporate world. Data driven insights are valuable because it empowers executives to make well-informed decisions that may improve operations, boost productivity, and forge closer bonds with clients.

Also Read: AI Reshaping Future of Data Center Industry – Google Shows How?

Challenges of Data Driven Culture

Challenges of Data Driven Culture

Although the benefits of a data driven organization are numerous, creating a data driven culture has sometimes been challenging. Recent research by NewVantage Partners revealed that businesses’ attempts to adopt data driven culture are failing. According to the research, 72% of survey respondents, primarily C-level executives (97.5%), said their firms still needed to establish a data driven culture. As a result, we’re witnessing a dismal digital retreat rather than an increased digital revolution.

It may be helpful to compare your company’s data consumption to your smartphone usage if you’re trying to determine the magnitude of your data driven culture. Today, most people would be upset if their phones were lost for a long time. However, most of us only understand how reliant on these gadgets (and their applications) we are once they break or disappear. What would happen if your team couldn’t access your data for a whole day in a similar way? What about a full week? What would your organization’s response score on the following scale be if you were to rate it?

Also Read: 4 Key Areas to Apply Data and Analytics in Digital transformation (DX)

What Does it Take to Build Data Driven Culture?

What Does it Take to Build Data Driven Culture

When employees understand the driving metrics for which they are accountable and how those metrics affect the Key Performance Indicators or KPIs, a firm is said to have a data driven approach. Data needs to be made more accessible to the general public, or “democratized,” in other words. Employees must comprehend data and apply it to make appropriate decisions for their responsibilities within the organization. Citizen analysts are required, as they can do fundamental analyses without the assistance of a data staff. When all workers use the same data to inform their choices, the business also requires a Single Source of Truth. To keep data uniform, accurate, usable, and secure, it must have data governance and master data management.

A corporation is more data driven culture reliant if these indicators are better. These may appear complicated, but they are not. These elements will fall into place after you properly begin your data driven approach journey.

Data driven approach is not, however, a one-time endeavor that can be abandoned after completion. Data maturity, data driven Leadership, data literacy, and Decision-making process are the four pillars of a data driven culture. Therefore, when creating a data driven framework, these 4Ds are crucial.

However, the underlying culture is the biggest challenge to the data driven approach. Therefore, we have condensed the critical steps to develop and maintain a culture with data at its heart.

  • It starts at the top: Top managers in organizations with a robust data driven model frequently create the expectation that data must support choices and that doing so is a standard procedure rather than anything unusual or innovative. They set an example by doing. C-suite executives at one retail bank collectively evaluate the data from controlled market trials to make decisions about product releases. At a top IT company, senior executives study in-depth summaries of ideas and the data supporting them 30 minutes before meetings so they may act on the information. As a result, data driven company employees who want to be regarded seriously must speak to top executives on their terms and using their language. As a result, these habits spread downward. Significant changes in company-wide standards can be catalyzed by the example set by a select few at the top.
  • Pick the metrics that suit you: By carefully selecting what to track and the metrics they want their team members to utilize, leaders may significantly impact how people behave. A data driven company can benefit by predicting pricing changes made by rivals. Predictive accuracy over time is a statistic that may be used to measure this. Therefore, a team should constantly make specific forecasts regarding the size and direction of such shifts. The accuracy of those forecasts should also be monitored because they will improve over time.
  • Don’t cage your Scientists: Businesses not only push data science closer to the company but also pull the business toward data science, primarily by requiring that staff have a strong understanding of coding and quantitative concepts. There is no need for senior executives to reincarnate as machine-learning engineers.
  • Train Personnel: Many businesses spend on “big bang” training initiatives, but if workers don’t immediately put what they’ve learned to use, they quickly forget it. Therefore, while foundational training of the data-driven company should include basic skills like coding, it is more efficient to teach workers in advanced analytical ideas and tools only before they are required, such as a proof of concept. One store only trained its support analysts on the finer parts of experimental design just before a first market trial. The data driven solutions knowledge persisted, and now analysts use words like statistical confidence that were formerly foreign to them.
  • Be Flexible: Numerous data driven culture businesses that rely on data are home to various “data tribes.” Each may have preferred information sources, custom metrics, and programming languages. This may be disastrous for a whole company. Trying to reconcile somewhat divergent interpretations of a measure that ought to be standard can cost businesses endless hours. Consistency issues in modelers’ work also have an impact. Every move by analytical talent requires retraining if coding standards and languages differ throughout a corporation, making it challenging for them to move around. If ideas always require translation, discussing them within might be difficult. Instead, businesses should use standard measurements and programming languages. One international central bank achieved this by requiring Python coding skills from investment banking and asset management recruits.
  • Be Analytical: Rarely is there a single, ideal strategy for most analytical issues. Data driven solutions scientists must instead make decisions involving various tradeoffs. It’s a good idea to ask teams how they went about solving an issue, what options they thought about, what they thought the tradeoffs were, and why they decided on one solution over another. Teams gain a more excellent knowledge of the techniques by doing this often, frequently inspiring them to think about other options or reevaluate underlying presumptions. One primary provider of financial services initially believed that a machine-learning model to detect fraud couldn’t operate swiftly enough to be employed in production. However, it was later discovered that the model could be made incredibly quickly with a few adjustments. When the business started using the model, it saw astounding improvements in detecting fraud.

Also Read: Data Strategy – A Key Checklist for Digital Transformation

Final Thoughts

Data may offer evidence to support assumptions, enabling managers to go into new processes and areas without taking a blind leap. However, more than merely wanting to be data driven is required. Data driven companies must create environments where this thinking may flourish if they want to be data driven. Leaders may model this transformation for others by adopting new behaviors and setting standards for what it means to base choices on facts. However, modeling a data driven approach is easier than it seems, as it is a cultural shift one must embrace. This is where Stevie Award winner Veritis comes to your rescue. We understand your requirements and will help you transition seamlessly. Reach out to us to embrace data driven culture better than your competition.

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