5 steps to increase business resilience through data maturation

The role of data in empowering business leaders to make better-informed decisions is apparently growing.

But, as the world economy becomes increasingly complex and volatile, more and more digitally mature companies are proving to be resilient to economic shocks.

In 2021, Danny L. Id, North America director of digital analytics software firm Adverity, conducted a study on the subject. His research examines the strategies and tactics of 10 companies that have enabled them to be more data-driven and looked at their performance during two recent recessions: the Great Recession of 2008 and the Great Lockdown of 2020.

El-Id’s research shows that there is a strong link between data maturity and the resilience of economic shocks. He further emphasized that there are five major indicators of data maturity.

What is data maturity?

In terms of what is meant by data driven, it refers to a mindset and culture among companies to consider or make decisions based on data and allows algorithms to make the necessary predictions based on data rims for a certain amount of time. And to make judgments by believing those algorithms, then to apply judgments to recommendations that people make predictions or data disclosures.

Data maturation or digital maturation refers to a scale or a kind of benchmark where companies are able to determine what is sitting on the data maturation scale.

El-Id says: “So you have an early stage of data maturity, which is new, and basically your data sits in a silo in an organization where no data company talks to each other. So IT has its own data that they process, sell. And marketing can be theirs, operations can be theirs. And often in new data mature companies, they’re not really connected.

“Then the next step is an emerging data maturation process where some data starts talking to each other. So maybe your sales and marketing can start talking to your operational data, but it’s still isolated.

“Then you are connected and have many moments. So really connected where you start to do more integration across the organization. You are able to get a complete picture of all the data passed through the company. And then many moments actually mean being able to pass the estimates or predict the result based on the complete data point of the signal.

“Telcos do a good job at this, because they only get through their customers, the hardware they have and all the different wireless stations. So they go through all that data and they can consider things like weather data. They can consider many different signals. And when a company reaches that stage, they can make really impressive and accurate predictions based on behavior and geography, and from there it starts to become really strong. So when we talk about data-driven culture and data maturity, it’s a bit of a scaled-down approach. “

5 major indicators of data maturity

1. Culture and leadership

Creating a data-driven culture requires an organization’s leadership team to adapt its business model to how it thinks about data. Executives must decide that turning data into an asset is a top priority, and formalize the inclusion of a Chief Data Officer (CDO) in their position to help centralize and expand the role of data across business strategies.

El-Eid says: “I have interviewed many companies in different industries, at different levels of maturity, at different levels of classification.

“Leaders who valued and actually invested more and realized the benefits of being data driven were actually more successful because of the data driven mindset. Many companies are clustered in emerging and connected stages, and many companies, especially at the top, may overestimate how data driven they are.

“But as you go down the stairs, you will see that it is less and less accurate. But those who were using the technology had a really deep idea, investing in both the hard IT factor and the soft factor. And it was a clear picture of how it spread throughout the organization. These companies have been more successful.

“And again, these would mean that you have to spend for these processes.” At Telco you have a tendency to have senior executives who are quite technical. And have their fingers on the pulse. Concerning how data is being used at all levels of the organization, however, those that were emerging in newer and newer stages – I’m referring to some companies in the travel industry and hospitality, for example – they overestimate the trends in how they operate information. It’s much more silent and isolated than they actually claim. “

2. Develop DataOps

A company cannot be data-driven if it does not focus on creating technology stacks to enable it.

And to turn data into an asset, a company needs to audit both structural and unorganized data before investing in a robust digital infrastructure to process it.

El-Eid says: “Dataps are an issue that companies are increasingly focusing on. When I started writing about it, for my thesis, it wasn’t something that was commonly known, or a word used in an organization. You have DevOps. That’s what usually falls under it. But DataOps has a wider impact with understanding the workflow and how architecture and design of data pipelines within organizations. This is part of building an infrastructure so you can start integrating all your data.

“So it’s an increasingly effective guideline that companies are starting to hire. When they start assigning or adding CDOs with their rankings, and C suites and boardrooms, in general, it falls under its command to make the data truly effective so that it becomes an asset within an organization and not just, let’s say. , Insights, or statistics. Turning data into a real asset with a dollar value is essentially its outcome and purpose. “

3. Data and technology investment

To ensure a strong data architecture, companies need to prioritize investment in data and technology. After the Great Depression, JP Morgan’s total annual spending on technology rose to $ 8.5bn in 2011; That same year, it was estimated that hedge funds would spend an additional $ 2.09 billion on IT.

El-Eid says: “Based on my research, most companies have not been successful in digital conversion and IT investment. They have not created the culture necessary to be able to take advantage of technology. They did not properly create a roadmap or infrastructure to get the most out of it.

“So they start throwing money, let’s say, big names, because they hear Salesforce, or they hear IBM or SAP and they throw a lot of money at these companies. But then, basically, we always hear that transfer or transformation never ends. It is definitely an ongoing work. But you have to have a specific milestone and be able to map it. So that you can evaluate your ROI from it. And a lot of the companies I’ve talked to – I think it didn’t deliver about 70% of the expected returns and they blame technology. “

4. Upskilling

Several studies have found that the main internal barriers to enabling a data-driven workforce are gaps in culture and talent skills. In his third annual CDO survey, Gartner found that “poor data literacy” was the second biggest obstacle to success, ahead of the “culture challenge to adapt to change”.

El-Eid says: “Imagine the need to go deeper into understanding how a data architecture is built, how different ends communicate with each other, where to go and how to get the data needed to be able to make certain decisions. Companies will either rely solely on the IT department, and this creates a huge hurdle, because you have all the people in different departments. They don’t want to deal with it, or they don’t have the skills. So they will funnel everything in the IT department. And that’s going to create a huge barrier, and then move nothing.

“That’s why companies need to upgrade or re-scale their workforce so that their data literacy is spread across the organization.” So that someone, if they need to, can go and source the information they need. So they are familiar with being able to deal with the necessary platforms.

“And it takes a lot of effort, especially in large companies, to re-engineer everyone or get them to adopt a new platform. Most people do not want to learn anything new. So that part is really generating a lot of delays. It’s the human side. And so we often hear in their own company blaming the seller or the technology instead of looking at the human side of it that they didn’t succeed. “

5. Automation

Another reason to evaluate a company’s digital maturity is its ability to effectively manage automation. A company is in a strong position to use automation when it achieves satisfactory levels across key pillars like people, processes, technology and data. In general, the main goal is to reduce costs and improve performance.

El-Eid says: “Not all processes and works and organizations will benefit from our automation. It is not always the solution to the problem. Not always trying to automate everything fixes the underlying issues that may be there. So since companies have a broad spectrum for automating specific tasks, we will see that these will benefit the most from digital and data investments.

“It really refers to how prone a company is to be able to automate certain processes and tasks versus those that may not be needed. No need to automate. “

Danny L-Eid will take part in a panel discussion on June 23 at the DMWF. ‘We’ve all got modern technology but we’re still not data-driven – who’s to blame?’ In the discussion of the title:

  • What problems can technology solve (and what problems can’t it)?
  • What does data-driven culture mean and why is it important?
  • Rent or high efficiency? What role do employees play in getting the most out of data?

Tags: Adversity, data, data maturity

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