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How To Ensure Better Data Culture In Organisations

How To Ensure Better Data Culture In Organisations

Sameer Balaganur
data science culture

Due to the proliferation of data, companies across the world are able to make better decisions, innovate more and integrate new ideas into their daily functions. Data analytics have been widely explored to satisfy customers, improve services, streamline operations and formulate long-term strategies.

But without a clear understanding of how data can help across functions, its true potential cannot be leveraged. In other words, a good data culture needs to be embedded into the fabric of every organisation so that they can make the most of the available data to help in critical decision making.

Here, we explore some of the most critical points when it comes to improving the data culture in organisations:



A Robust Management At The Top

Most companies that boast of a good data culture have capable managers at the senior level who set a good example for teams to take the support of data in all the decisions they make. In some companies, managers spend a lot of time using data as a piece of evidence to support product launches. Other firms may have a culture where managers invest a lot of time in meetings reading detailed information on proposals with supporting facts so they can take an evidence-based action as a response to it. These examples will serve as a lesson to junior managers and employees to employ data wherever possible, until it becomes a norm across the organisation.

Single Data Source

In large organisations, data sources are often isolated in independent systems. Employees can pull the same metric from different systems and get different results. A data source is the single source of truth, that may be a data warehouse, which can play a critical role in ensuring consistency in tracking and merging data. By keeping all the data in one place, the hassle of identifying incorrect data and relying on assumptions to scale is eliminated by a large margin.

Creating Open Access

The most common complaint by employees across businesses is that they struggle to acquire even the most basic data for their work needs. This is a common occurrence, even though a lot of effort has recently gone into democratising access to data within organisations. One of the approaches could be putting a system in place along with a right governance where it requires simple self-servicing to access data based on needs. Some organisations instead of implementing a program to reorganise all the data present, grant all-inclusive access to a few key measures at a time. This makes it easy for employees to pick some metrics specific to their needs, further encouraging them to make more use of the data source.

Promoting Data Literacy & Help Employees Learn With Real Data

With a broad access to data, there must also be a good understanding of it. Organisations should provide training to their employees to ensure that essential data literacy is provided across the entire employee base.



At the end of the training:

  • An employee should know the basic ways of summarising data and should be able to tell useful data from a less useful one
  • The employee should be able to communicate insights or concepts to speed up understanding and problem solving within a team

Empowering employees by giving them an opportunity to deal with real data along with giving them better access can help them implement some of the lessons learned in training sessions. This will help them understand how data can be leveraged to save time, avoid rework, etc, promoting better productivity.

Decision Making

Many decisions in a team are dependent on few people occupying higher positions in an organisation. Leaving critical decisions to them can sometimes be inadequate when these choices are made based on instincts or experience, when it should actually be coming from a tested and supported point of view with statistical proof. One of the ways to deal with this is experimenting with A/B testing. A/B testing is done by comparing two versions, say, of the web, email or any other marketing asset, and measuring the difference in performance. After measuring the changes, one variation is given to one group and the other to the other group, helping make the best decision for a given situation.

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