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New Survey Shows Why Making Decisions Isn't Always Easy

Recent survey highlighted how organizations are leveraging data to make more intelligent and productive business decisions. Exasol's CTO, Mathias Golombeck, shares his insight into the company's survey results.

• By James E. Powell
• November 6, 2019

Analytics database developer Exasol recently released the results of a survey highlighting how organizations are leveraging data to make more intelligent and productive business decisions. We spoke with Exasol's CTO, Mathias Golombeck, to learn more about what conclusions we can draw from the responses.

For Further Reading: Anatomy of a Data Strategy: From Operational Intelligence to Artificial Intelligence  Overcoming the Roadblocks to Analytics Adoption Data Quality Best Practices for Today's Data-Driven Organization

Upside: One piece of interesting news I found in your survey was that of the 1000 IT decision makers you polled, 80 percent reported that data guides organizational decision making more than half the time. Does that mean the days of decision-by-gut-instinct are coming to a close?

Mathias Golombek: I believe that the era of data-driven decision making in companies is on the rise. Data is increasingly regarded as the most important asset in corporations, and companies that apply advanced analytics gain competitive advantages.

This doesn't mean that algorithms are taking over every single decision in an organization, but having some data helps build an opinion. One example is our customer Revolut whose HR department analyzes the correlation between questions during the interview process and the actual success of a hire. With this analysis, they were able to grow rapidly while optimizing their hiring process constantly. There are many areas of business that can be optimized automatically by applying operational business intelligence, and it's exciting to see the impact data can have on outcomes.

Over a third (39 percent) of survey respondents named data security as the biggest obstacle to their data strategy. What industries do you think will be held back from data analytics because of security issues and why?

In more regulated industries such as the pharmaceutical, healthcare, and financial industries, security trumps data strategy. On the other hand, security issues don't have to hold data analytics back. It's simply an area that organizations have to focus their data strategies on to make sure they can leverage the power of their company's data without jeopardizing security.

Almost a third (31 percent) of respondents named slow data performance as an obstacle. Is that because IT hardware isn't able to catch up or data volume growth is outpacing hardware performance (or some other cause)?

There are multiple reasons why legacy data management systems cannot meet the requirements of modern data analytics strategies. For one, the number of data sources has exploded -- from mobile phones, machine sensors, social media, weather forecasts, etc. -- which means a staggering amount of data that must be captured, stored, organized, and analyzed.

Further, more data users want access to data. Although mainly the financial department and top management expected reporting into data insights 10 to 15 years ago, today nearly every department in a company wants to make data-driven decisions, from sales and marketing to development and HR, and up the logistical production chain.

Finally, the complexity of analysis has increased over the years. Fifteen years ago everybody was mainly talking about cubes and aggregations of numbers. Today, data scientists can pick their ideal data analytics methods for the specific problem, up to applying artificial intelligence and machine learning.

These challenges have created a lot of pressure on the BI and data analytics departments, which often have moved from internal IT to more prominent departments, be it under the CFO or even a dedicated chief data officer or chief analytics officer. That's why new modern data analytics platforms are being adopted, often within a two-tier strategy with cost-efficient data lakes and operational analytics layers on top (mostly a parallel RDBMS building a performing data warehouse/access layer).

You've said that the survey results show that enterprises are ready for a data-driven change but they don't have access to the tools they need to achieve it. What tools, specifically, are they missing?

BI tools are spread widely across companies, but data users are often quite limited in either performance or data accessibility. Central data warehouses are often unusable for a larger audience and not all data sources are integrated -- so it's hard to leverage the full power of a company's data treasure. Often, users have to work on limited data marts without fast response times, making it hard to work interactively with data.

Your survey found that the business areas most in need of a data-driven strategy shift include sales, operations, and marketing. Aren't those the traditional end users who have always needed business intelligence?

These are indeed the traditional end users, and it's not surprising that they are facing the most pressure to become data-driven because they have the most important impact on business success. It's interesting that other departments (such as HR, logistics, or even legal) have also reached a significant level of need.

A quarter of respondents said their enterprise has appointed a chief data officer or chief analytics officer. Is a CDO or CAO right for all enterprises, and if so, why haven't more companies hired one?

CDOs might be relevant only for a certain size of companies, for example, ones with more than 1000 employees. Additionally, not every company has identified the important value of data being core to a company's strategy, but there is a clear shift towards that.

What survey results met your expectations? Were there any surprises?

I was surprised to see the limited range of where data analytics was being applied in the enterprise. Areas such as sales and customer satisfaction have been the focus of data for some time, but with the tools available to enterprises, there's the opportunity to do much more with data than what many businesses are currently doing. With improved data strategy and the tools to make it happen, data analytics can be applied to many more business groups, including HR, R&D, and finance.