5 Ways to Build a Data Driven Culture

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Tech Insights for ProfessionalsThe latest thought leadership for IT pros

Wednesday, September 30, 2020

If you’re searching for ways to successfully transform your business into a data-driven organization, you’re not alone. Tapping into the potential of data has become a contemporary holy grail, and for good reason.

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5 Ways to Build a Data Driven Culture

A study by researchers at MIT last year found that companies with data-driven models achieved an increase of around 6% in output and productivity. A successful data strategy can transform a business into an agile organization, able to quickly adapt to dynamic circumstances.

But what exactly does a data-driven organization look – and act – like? Any business model that employs data utilization as the primary force to optimize its processes is data-driven. Of course, all businesses handle data on some level, but if your decision-making and revenue streams are powered by that, then you’re a data driven organization.

The benefits to this model are clear, but it can be difficult to achieve; it takes patience and careful consideration. Here’s 5 tips to help implement a data driven culture in your organization.

1. Identify underserved departments

Despite the drive for data transformation, many departments within organizations remain mired in messy and inefficient models. Although a data-driven culture needs to be top-down, it must also be bottom-up. By identifying teams within your organization that could benefit the most and implementing new processes there first, you can kick-start your transformation in the most impactful way.

For example, some marketing departments experiment with different digital tools to reach potential customers and create leads, entering and analyzing the resulting data with painstaking effort into manual spreadsheets. As a CIO, you could save them time, money, and unnecessary human error by getting your tech-savvy team to integrate automated processes.

Make sure to communicate with the different departments within your organization. By learning the different methods they use, you can identify under-resourced departments and begin your data-driven revolution with optimum impact.

2. Enable agile analytics processes

Using data correctly can help to take some of the uncertainty out of your business decisions, but it’s vital that also you empower your employees to experiment with it.

Maintaining this sort of agility when it comes to analytics enables experimentation and the development of innovative solutions. The analysis of data rarely produces straightforward answers; your algorithm or machine-learning program might underperform or otherwise highlight missing data you might need to fully assess the problem you’re looking to solve.

Encourage your team to avoid rigid practices or expectations when they’re analyzing data.

“Look to capture learnings in the backlog and decide which discovery and implementation options are worth prioritizing.” - Isaac Sacolick, President of StarCIO

 

The analysis of data is a discovery process, so you should be open-minded about what it might yield and willing to implement or act on unexpected findings. This way, you’ll ensure you’re driven by the data, as opposed to any preconceived idea of what it should tell you.

3. Fix data access issues

Fixing data access issues is an integral part of a data-driven culture. It’s something that can be done easily and transform the speed and efficacy of your decision-making.

Use automated processes and open-access platforms to democratize data within your organization, making it easily accessible to everyone who might need it. However, be careful not to fall into the trap of using inefficient programs that reorganize all of your data at once. Instead, grant universal access to a few key metrics at any one time, based on C-suite priorities.  This will make the most important data available immediately, giving your analysts the information they need to help drive the business’s core agendas.

Over time, you can expand capacity by tying more numbers to this data source, gradually encouraging its use throughout your organization. 

4. Introduce proactive data governance

You might be tempted to spend a long time developing a data governance and usage policy before you start introducing analytics capabilities. While that does seem to be a logical course, it can be extremely challenging to encourage a coherent governance policy without being able to see the data first and deciding on further capability requirements.

You still need to establish clear usage and security guidelines, but you can do this proactively by driving them alongside your analytics initiatives. This is, perhaps, the definition of being driven by the data. A proactive governance policy further reinforces the agility that any organization needs to enable ongoing improvement in its operations.

5. Train staff to leverage analytics

Data visualization platforms like Microsoft PowerBI and Tableau have given organizations the capability to design intuitive dashboards. Even so, some dashboards may miss the mark on best practice when it comes to visualization, which makes it difficult for end-users to use that data effectively.

You can overcome this challenge by providing training to your staff on what the underlying data represents, developing clear and coherent policies that enable them to leverage the analysis of that data to make decisions, and designing change management processes to enable adaptation where required.

An integral part of being a successful leader is the ability to empower your employees to use the tools available to them to make decisions for themselves, and this is no different within a data-driven culture.

There are a myriad of ways you can build a data-driven culture within your team or organization. The key is to reduce inefficiencies and build agility into any framework you develop. Data, by its nature, will change over time, so it’s crucial that you and your team can adapt with it. Truly embedding a data-first approach can be extremely challenging because it involves the scrapping of legacy systems and the transformation of mindsets, but the benefits it can reap are well worth the time taken to put these strategies into place.

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