6 steps to build a data-driven company, according to experts


Over the past year, companies have had to adapt quickly and adapt to new and varied patterns of customer behavior, while their previous models were abandoned.

Those who stepped up their analytics capabilities were better able to deal with huge changes in user behavior and the economic environment, as data analytics became “an essential navigation tool,” according to main partners at McKinsey.

2020 has told us loud and clear that it would be unwise to always assume that past performance can predict future results. But the relentless increase in computing power and storage, coupled with an ever-increasing number of data sources, also means we no longer need to rely on gut decisions.

Trends can be observed and patterns identified from the vast amounts of customer and operational data generated by businesses. And those who know how to capture and use that information to chart the course of their business will be ahead of the game.

Becoming data-driven is not a “ good to have ”

There are great rewards for companies that integrate big data and analytics into their operations. According to PWC, these organizations benefit from 5% more productivity and 6% more profitability than those without.

In the meantime, Harvard Business School found that companies that make data-driven decisions are more confident in those decisions, proactive, and able to achieve cost savings. The trust coin is backed by Off the Shelf Analytics founder Adrian Coy, who told The Next Web:

“Being data-driven actually allows organizations to take more risk because they know they can quickly see when it’s not working and correct the course if necessary.”

Sainsbury’s, one of the UK’s largest supermarket chains, had to scramble to meet demand as panicked shoppers purchased essentials including canned flour and pork. Group CIO Phil Jordan told diginomica that its retailer was the first to use the data to identify elderly, disabled or vulnerable customers and ensure they had access to shop.

In addition to serving the bottom line, data insights can support decision making in other areas of the business such as employee engagement and corporate social responsibility. This can lead to a better understanding of the mindset of staff and the effectiveness of different CSR initiatives.

The problem is, turning data-driven insights into impactful business decisions isn’t a straightforward process. In fact, a recent to study by NewVantage Partners found that only 24% of those surveyed believed their organization was data-driven (up from 37.8% the previous year).

According to experts, if you want to build a truly data-driven business, you can’t miss these six steps:

First step: create a roadmap

The first step is to create a solid foundation by fully understanding the data you are working with and creating a roadmap for obtaining information. It involves defining how you understand, measure, and segment customer needs and behaviors, as well as how you build models to predict future behaviors and define the right KPIs, metrics, and processes.

Mike Bugembe, author of Cracking the Data Code, said in his book that the lack of a clear strategy can lead to spending time and money collecting and analyzing data that looks interesting and fair. strength prove to be useful. He wrote that generic exploration is great if your business has the time and human resources to devote to it, but if not, the business could remain data rich but information poor. “This jeopardizes any expected return on investment, as data is only valuable when used appropriately to deliver real results,” Bugembe wrote.

Second step: fostering a culture of mastery of data AND the democratization of data

Next, it’s important to “put the right metrics in front of the right people,” Coy said, through tools for reporting and analysis. People within the organization need to be able to understand the right KPIs – which should have been set at the founding stage – and define the right way to track them. This speaks to the need for a high level of data literacy within the organization, in addition to a data democratization culture where non-technical users can access the data.

Coy said business intelligence tools are useful at this point and people should be trained on how to use them and understand the decisions to be made, not just which buttons to press to get the report. Some of the tools used for data analysis are made by Qlik, Tableau, Pensée, PowerBI, Looker, Sisense, Spotfire, Yellowfin, Targit, DataRobot, and Snowflake, among others.

Ross Perez, Global Product Marketing Manager at Snowflake, told The Next Web: “These tools are essential for accessing your data, asking questions about it, evolving a strategy and ‘implementing’ that strategy.”

Step 3: Develop data storytelling skills within your team

To underscore the essential nature of data education, Perez said that as much as it is important to ask questions about data and allow it to tell a story, it is just as important to teach the people who work with data how to effectively tell a story with data.

Good data storytellers have the ability to convince and persuade. Studies have shown that tables and graphs can make people change their minds more easily than words. In addition, according to Andy Cotgreave, Technical Director of Evangelists at Tableau, one of the best ways to keep an audience engaged is to present the data in a way that is easily understood by anyone.

Step 4: Earn Level C Membership

The fourth step is to create a top-down culture for senior management so that they can ask for the evidence behind the recommendations and know how the results will be followed up. C-level buy-in to being data-driven is crucial, as is the direction that senior executives can provide.

A McKinsey Report found that executives who recognize the importance of data are open to receiving educational workshops. This leading-by-example demonstration incorporates the culture of data literacy among other staff members. And leaders who fully embrace a data-driven culture can ensure that data is as accessible as possible, one of the tenets of data democratization.

Without the support and understanding from top management, insights teams can be left without a rudder, without a clear path to follow or clear priorities as to what problems to solve and what data to analyze. This can cause an insights initiative to fail because it fails to gain traction or meet expectations.

Without strong data leadership, Bugembe wrote, organizations can fall behind their competitors in terms of winning, serving and retaining customers.

Fifth step: create a capacity for “ consultative ” analysis

Next, build a “ consultative ” analytical capacity capable of inventing approaches to answer big, important business questions. This is the time when you search for patterns everywhere and see which data points have a positive or negative correlation. According to Coy, this is important because consultative analysis can help companies determine the right questions to ask of data and the right requests to make.

Through the prism of the pandemic, an ordinary question might be about increasing or decreasing sales, but a question formed through a consultative approach would be “what should I do differently?”

Step Six: Assign Responsibility

Finally, make sure that there is someone who takes responsibility for the data. This can take the form of an analysis manager or a data manager.

As the results are manifold and democratizing data means putting accountability in the hands of many, Coy said that, at the end of the day, there needs to be clear accountability so that data is used in the right way. He said: “It all depends on the proper execution of the data. Bad data is worse than no data. Make sure someone is responsible for it. “

While Perez didn’t explicitly say whether there was a right or wrong way to collect information about the data, he did say that some activities were more beneficial than others.

One of the less profitable data activities, he said, is choosing a business strategy and then looking for data in retrospect to back it up. “Worse yet, use the data in a way that obscures the truth so that some trends seem stronger than they are and other trends disappear altogether,” Perez said. “It’s really counterproductive.”

Conversely, he said that if people are trained to understand and use data appropriately, they will be able to reap all the benefits that the data-driven approach can bring.

To understand how data can transform your business, see, “The evolution of data in the cloud: the keystone of competitive advantage”, research sponsored by Snowflake.

This article is brought to you by snowflake.com.


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