Leaders Need AI To Keep Pace With The Data Explosion

Data is exploding. Humanity’s current rate of data creation has us doubling the world’s data every two years, and this pace is expected to increase, not decrease. By 2025, the amount of data will double every 12 hours—suggesting that humanity will either be twice as smart or half as smart between the times we wake up and go to sleep.

This wealth of data, created exponentially as we go about our lives, has the potential to change the ways we live, work and invest—but only if we accept that, as human beings, we cannot absorb and process this information on a daily basis. To keep pace, we will have to partner with artificial intelligence to augment our own capabilities

Corporations have been working on becoming more data-driven for decades, and in some ways, they have been successful. On the other hand, research shows that about 70% of the data painstakingly collected and stored by companies goes unused, and what is actually utilized is often misused. So it is certainly an open question of whether the growth of data, documenting customer attributes, product strengths, production capabilities, salesforce effectiveness, employee engagement, and much more, will actually help companies make better decisions at all. The question isn’t whether the data is useful, because it certainly is. Instead, the question is whether or not leaders will accept that machines are the only way to remain competitive.

Historically, the use of data has been a laborious process—something done more often on a one-off basis as opposed to at scale. The steps are as follows:

1. The data is gathered. This requires finding a source for the data, formatting it and labeling it for use, and finding a place to store it.
2. The data is cleaned. The cleaning process includes removing outliers, eliminating duplicate or inaccurate data, filling missing data, and occasionally normalizing and creating metadata.
3. The data is analyzed. Usually the data crunching is led by an analyst who brings their own insight and hypotheses as to what the data should reveal. The actual analytical methods vary from simple trend analysis to complex regressions and more.
4. The data is reported. Leadership teams are not going to review spreadsheets and database files for insights. Instead, the analysis must be clearly visualized and served up in a report or dashboard.
5. Recommendations are made. Based on the analysis, the leadership team must determine how to act upon the data.
6. Actions are taken. Leaders, like investors will act on the insights and recommendations in their pursuit of better performance.
7. Results are measured and reported. Once leaders act, the results of their actions will be monitored and reported, both internally and externally. Those results will be used to judge the success of their actions.

Currently, most leaders and their teams and boards follow this using spreadsheets and a bevy of internal and external analysts. It’s no wonder that most of the data sitting in data lakes and data warehouses goes untouched.

We know, however, that leaders who figure out how to leverage this increasing data trove to improve their decisions and outcomes will produce superior returns, just like the best investors do that have long relied on machines and “quants.” Failing to make use of the growing surge of data will mean a significant handicap for any leader and their team just like it does in the financial markets.

The answer is for corporate leaders to use artificial intelligence to facilitate and speed up the steps above and in the process, make faster, better decisions. Although we admit freely that talented analysts and business people with AI knowledge will always be a part of the process—in fact we recently wrote an article about this key talent pool—AI solutions will rapidly grow in breadth and depth, overtaking today’s spread sheets and consultants. Today, AI tools can help analysts gather data and clean data, and even select relevant features. Of course artificial intelligence offers up myriad tools to help with data processing. Further, a variety of new startups are creating monitoring and reporting systems with dashboards to make that analysis instantly relevant, viewable, and useful for leadership teams in companies of all sizes.

Ultimately, the agility and real-time understanding and advice these types of systems offer will become as essential to enterprises as the enterprise resource planning (ERP) or customer relationship management (CRM) systems that are running their operations today. However, not all companies are building (or buying) their AI-enabled analysis systems at the same rate—and they are risks to the laggards. New research from the McKinsey Global Institute indicates that a handful of companies are quietly building an insurmountable AI advantage. Insurmountable because the capabilities, data, and expertise they are amassing will not be easily replicated by any company hoping to be a fast follower.

So what should you do as a leader to keep pace?   How can your team begin to make use of this expanding wealth of data in a scalable way? Given the many application of artificial intelligence, the answer is build, partner, and buy—all three.

• Build AI applications that deal with proprietary data and key internal insights unique to the company.
• Partner with AI specialists who can supply the tools you need to speed up the development process.
• Buy start ups with unique IP in your space that you can immediately leverage for a leg up.

Wherever you decide to begin, the key is to start today. The pile of data continues to grow and the faster you build the capability, the sooner you will begin to use it as a competitive differentiator.