Change Management WalkMe TeamUpdated December 8, 2021

Why Aren’t Your Data-Driven Methods Working?

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Why Aren’t Your Data-Driven Methods Working?

Data-driven methods aren’t just advantageous for the modern organization, they are essential.

To stay competitive in the digital-first next normal, businesses must leverage data to gather insights, inform decision-making, and tune into current trends, among other things.

Below, we’ll learn why data-driven methods are so important, then discuss how to foster a data-driven business.

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Data-Driven Methods Are the Future

Data is the future of business, and you can be a part of that by using data to make more informed decisions about your marketing, your product design, or your business overall. 

According to Gartner, a number of trends will continue to drive data and analytics in the years ahead, such as:

  • AI that is smarter, faster, and more responsible
  • Users will be able to combine components from several data solutions 
  • Big data will transition to “small and wide” data
  • Decision intelligence, which combines a number of disciplines including data and analytics, will become more prevalent
  • Data and analytics will become a core business function
  • Graph analytics will be used by 30% of organizations worldwide for rapid, contextualized decision-making

If these predictions are on target, we can conclude that data-driven methods – already widely accepted – will become the norm in the years ahead. 

The savvy business professional, however, will naturally want to know the benefits of adopting data-driven methods before actually implementing them in their organization.

Why Use Data in Your Business?

Data is your most important asset, because no matter what business you’re in, you need data to make objective decisions – and the more data you can collect and analyze, the more objective your business insights will be.

Among many other things, data can be used to:

  • Analyze and improve business performance
  • Determine what your customers want
  • Learn how to improve your products
  • Understand what areas of the market you should focus your efforts
  • Enhance employee productivity
  • Optimize workflows and business processes
  • Gain insight into competitors’ activities

To actually gain these benefits, however, it’s important to stay up-to-date with new technologies and the new ways of analyzing your data. 

Why Aren’t Your Data-Driven Methods Working?

Many businesses only leverage a portion of the potential value of their data. 

Answering the questions below can help you make more from your data-driven methods.

Are you tackling the four V’s?

For big data to be useful, many data management experts suggest tackling the four V’s of big data:

  • Volume. The amount of data.
  • Velocity. The speed at which new data is created.
  • Variety. The variety of data sources.
  • Veracity. The need to reduce noise.

By maximizing each of these elements, businesses will be able to have a sufficient quantity of usable data that delivers useful and actionable insights.

Are you hiring and retaining top data talent?

Talent management is especially important in the field of data management because this field moves so quickly. 

A shortage of data science talent makes talent management even more important than ever – not only is it harder to find top talent, employers must work harder to retain them.

Every business should have talent management systems in place to recruit, develop, reward, and retain skilled employees.

Here are a few ways to do that:

  • Create a data-driven culture
  • Incorporate data into the organization’s operating model
  • Develop campaigns specifically designed to attract data science professionals

Additionally, cross-training employees can help them become skilled in data-driven approaches. While not every worker can become a data scientist, they can become what has been called a “citizen data scientist” – employees who can use data proficiently and offer new perspectives on data.

Are you integrating data-driven methods into the business model?

Data-driven businesses are more efficient because they are making decisions based on tested data rather than gut feelings, emotions, or personal preferences. 

Since data isn’t driven by opinions, it can be used to design business processes that are more objective, systematic, and measurable.

Here are a few strategies that can improve an organization’s data capabilities:

  • Educate senior leaders, managers, and employees on the benefits of data
  • Lead the adoption of new data-driven methods and tools
  • Build a digitally savvy workforce
  • Research and innovate with emerging data technology

These are excellent long-term strategies. 

But where should you start if your business has yet to get started with data?

What steps can you take today to start implementing data-driven methods?

Companies that are low on the data maturity scale may feel a bit intimidated. After all, data mastery requires extensive investments and effort.

Here are a few actions that can be taken in the short-term to get the ball rolling:

  • Obtain buy-in from executives. It’s important that everybody in the company has a good understanding of data and how to use it to make decisions. The CEO needs to be involved, for instance, and there needs to be buy-in from everyone.
  • Democratize data. Business units should not operate in silos, they should operate as a cohesive machine. But unfortunately, all too many companies suffer from fragmentation. To make the most of data, it is crucial to democratize data and incorporate data-driven methods across the entire organization.
  • Build a data management function. One of the best ways to make effective use of data is to build a mature data management function. For instance, a few steps to take include: hiring a data manager, giving them the proper authority, initiating organizational changes, sharing data between silos, and creating business processes that leverage new and existing data.

These are good points to start from. But, as mentioned, it is important to engage in continuous improvement to maximize the value of the data function.

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