5 steps to increase business resilience through data maturity

5 steps to increase business resilience through data maturity

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

But as the world economy becomes more complex and unstable, digitally more mature companies are showing that they are resilient to economic shocks.

In 2021, Dany El-Eid, director of North America at digital analytics software firm Adverity, conducted a study on the subject. His research examined the strategies and tactics of 10 companies that allowed them to be more data-driven and looked at their performance during the two most recent recessions: the Great Recession of 2008 and the Great Blockade of 2020.

El-Eid’s study showed that there is a strong link between data maturity and resilience to economic shock. He also stated that there are five main indicators of data maturity.

What is data maturity?

In terms of what it means to be data driven, it is more about the way companies think and culture to consider or make decisions based on data and allow algorithms to make the necessary predictions based on the amount of data that accumulates over time. And trust these algorithms to make judgments, then have people apply judgment to predictions or recommendations that data reveals.

Being mature for data or digitally mature refers to a scale or type of benchmark to be able to define where companies are on the data maturity scale.

El-Eid says, “So you have an early stage of data maturity, which is emerging, and it basically means that your data sits in silos within an organization where none of the data companies talk to each other. So IT has its data to process, sales and marketing can have theirs, operations can have theirs. And often in mature companies that are born for data, they don’t actually connect. So, in inherited jobs, I’ll especially hear some of my friends tell me they don’t know what others are doing with data across the organization.

“Then the next step is to develop a data maturity approach where some of the data starts to talk to each other. So maybe your sales and marketing could start talking to your operational data, but they’re still pretty unrelated.

“Then you have more connected moments. So really connected is where you start to have more integration across the organization. You can have a more complete picture of all the data transmitted through the company. And then multiple moments really relate to the ability to draw conclusions or the ability to predict outcomes based on the totality of signal data.

“Telcoms are doing a good job at this because they only have so much data passing from their customers, the hardware they have and all the different wireless stations. So all that data passes, and they can take into account things like weather data. They can take into account so many different signals. And when a company reaches that stage, it can make really impressive and accurate predictions based on behavior and geography, and that’s where it starts to become really powerful. So it’s a slightly scaled-up approach when it comes to data-driven culture and data maturity. “

5 main indicators of data maturity

1. Culture and leadership

Creating a data-driven culture requires an organization’s leadership team to evolve how it thinks about data and to adjust its business model accordingly. Executive teams need to decide that turning data into assets is a top priority and formalize the inclusion of the Chief Data Officer (CDO) in their ranks to centralize and expand the role of data in business strategy and help drive vision.

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

“Those who had leaders who valued and actually invested more and understood the benefits of data management, data-driven thinking were, in fact, more successful. Many companies are grouped within the emerging and connected phase, and many companies, especially at the top, may overestimate how data-driven they are.

“But as you go down the ladder, you’ll see that it’s less and less accurate. But they had a really deep understanding of the technology used, investing in both hard IT and soft factors. And he had a clearer picture of how it runs through the organization. These companies were more successful.

“Again, we return to the example of telecom. In telecommunications, you tend to have more executives who are quite technical. And they also have a finger on the pulse. As for how data is used, at all levels of the organization, however, those that were emerging and emerging – I mean some companies in the tourism and hospitality industry, for example – tend to overestimate how much they are run. data. It is much more closed and unconnected than they actually claim. ”

2. DataOps development

A company cannot be driven by data if it does not focus on building a technology stack to make it possible.

And converting data into assets requires a company to audit all of its data, both structured and unstructured, before investing in a robust digital infrastructure to process it.

El-Eid says: “DataOps is something that companies are increasingly focusing on. When I started writing about it, for my thesis, it was not something that was generally known, or a term used in the organization. You have DevOps. This is typically what falls under it. But DataOps has broader implications for understanding the workflow and the way the data pipeline within an organization is designed and engineered. This is the part related to building the infrastructure so you can start integrating all your data.

“So, it’s increasingly an operational guideline that companies are starting to apply. As they start allocating or adding CDOs to their ranks, both the C department and the boardroom, usually, it would fall within his or her mandate to actually operationalize the data so that they become assets within the organization, not just, say, insights or figures. The actual transformation of data into assets worth dollars is essentially the result and purpose of it. ”

3. Investing in data and technology

To ensure a robust data architecture, organizations must prioritize investments in data and technology. After the Great Recession, JP Morgan’s total annual spending on technology rose to $ 8.5 billion in 2011; that same year, hedge funds were estimated to spend an additional $ 2.09 billion on IT.

El-Eid says: “Most companies, based on my research, do not have a successful outcome in digital transformation and the IT investments they make. They have not built the culture needed to reap the benefits of technology. They didn’t build a road map or infrastructure properly to get the most out of it.

“So they start throwing money at 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, in essence, we always hear that migration or transformation simply never ends. It is certainly continuous work in progress. But you have to have certain milestones and be able to map it. So you can estimate your ROI based on that. And a lot of the companies I talked to – I think it was somewhere around 70% didn’t give the expected return, and they blame the technology. “

4. Upskilling

Several studies have shown that the main internal barriers to a data-driven workforce are gaps in culture and skills. In his third annual CDO survey, Gartner found that “poor data literacy” is the second biggest obstacle to success, preceded by “cultural challenges to embracing change”.

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

“That’s why companies need to improve their skills or increase their workforce so that their data literacy is present throughout the organization. So that someone, if necessary, can go and get the information he needs. So, they are familiar with the possibilities of working with the necessary platforms.

“It takes a lot of effort, especially in large organizations, for everyone to retrain or to adopt a new platform.” Most people do not want to learn anything new. So that’s the part that really creates a lot of delays. That is the human aspect. And so we will often hear the blame on the supplier or the blame on the technology as the reason why they failed, instead of really looking at the human aspect of it within our own organization. ”

5. Automation

Another factor in assessing a company’s digital maturity is its ability to use automation effectively. The company is in a strong position to take advantage of automation when it achieves satisfactory levels in key pillars such as people, processes, technology and data. Typically, the main goal is to reduce costs and improve performance.

El-Eid says: “Not all processes, tasks and organizations will benefit from our automation. This is not always the solution to the problem. The fact that you always want to automate everything does not solve the basic problems that could exist. So, within organizations that have a wider range to automate certain tasks, we will see that they will benefit most from digital and data investments.

“It really refers to how much the company is inclined to be able to automate certain processes and tasks in relation to those that may not have to.” Therefore, those who do not actually have the capacity or requirements for automation, by default, will not go further along the data maturity scale, just because they do not have to automate. ”

Dany El-Eid will participate in a panel discussion at DMWF on June 23. The discussion entitled ‘We have all the latest technology, but we are not yet driven by data – whose fault is it?’, Will include:

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

Tags: Adverity, data, data maturity



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