Artificial Intelligence (AI) has permeated many different applications, from house-cleaning robots to self-driving cars and web-based smart assistants. With the proliferation of business data, it’s not surprising that AI is also impacting data analytics and BI tools being used in various industries.
Data analytics and BI landscape:
Traditional BI tools can no longer effectively process and visualize the large amounts of complex data being generated by numerous business systems, devices, and channels in the modern enterprise. It is no wonder that AI and allied technologies have featured in Gartner’s Hype Cycle for Emerging Technologies.
AI, by definition, refers to the training of computerized systems to perform intelligent tasks in place of human effort, such as problem-solving and even decision making. It is already being used in several enterprises to derive business analytics insights from large amounts of complex data. The recommendations provided by such AI-powered data analytics can then be accessed and harnessed by business users, even if the end-users themselves do not specifically have analytical skills.
For example, large-scale retail brands that have thousands of outlets spread out over large geographical areas are already using AI/ML-enhanced technologies to process the massive amounts of customer and product data that is generated on a daily basis. The insights from their business analytics software are extremely valuable, as they can lead to strategic actions that can help make business decisions. These business analytics insights can help them understand their customers better and make improvements in their merchandising and marketing departments to improve business efficiency and achieve sales growth. The data being generated by these large retailers is so massive that traditional BI tools cannot process it in time and stakeholders cannot aggregate and study it in time to make quick decisions that impact daily business decisions. Data that is not available in a timely manner is of little value for businesses – and therein lies the importance and value of AI-powered analytics.
AI-powered chatbots are no longer a novelty and are cropping up all over the world-wide-web. But, there are also business intelligence bots that are analyzing business data in an automated manner and unveiling insights from the data. It is expected that such BI bots with natural language processing capabilities will soon be enhancing the adoption of BI tools by enterprises.
Are CEOs taking AI-powered analytics seriously enough?
Several surveys have confirmed that CEOs are taking AI very seriously and the majority feel that AI-powered analytics is crucial to their business growth. But the question remains: do they know how to implement it? Or are they merely being driven by a bandwagon effect, fearful of being left behind while other competing organizations exploit the power of analytics to make better, more growth-yielding decisions?
The result is that, in spite of acknowledging the importance of using AI for business analytics], the adoption of these advanced AI/ML tools is still abysmally low.
CEOs also have to recognize that AI cannot be adopted without clear strategic direction from the leadership about which aspects of the business or which challenges can best be addressed by automated decision making from AI-powered tools. It is hardly enough to relegate such strategy to data scientists; while data experts understand the complexities of data, analytical models, and statistical methods, they are not in the best position to make strategic and long-term business decisions from an overarching organizational and domain-oriented point of view. The buck must stop at the CEO’s desk for this strategic thought leadership.
Without this clarity of strategic goals, enterprises are likely to get stuck in a rut of conducting trials and pilots without achieving significant results from data projects.
To be able to reap the real benefits of AI-augmented analytics and automated insights, the organization needs to foster a data-driven culture. This means that stakeholders and employees alike must place immense trust in data, consider business data to be extremely valuable, and believe that the automated decisions revealed by AI-powered BI tools are indeed superior to human decisions, simply because they are intelligently trained and fed with immense amounts of data – much more than the human mind can process.
Are CEOs leading by example?
So are CEOs leading by example when it comes to trusting data and allowing business decisions to be dictated by what data reveals? Or do they still trust their gut feeling more than data-driven insights?
To illustrate this problem in a little more detail, let’s look at a survey conducted by Deloitte in 2019. One thousand executives from large enterprises (500+ employees) answered the survey. A whopping 67 percent of respondents said that they were not comfortable using data from their company’s data tools.
This is a very significant finding, as it means that even in organizations where valuable business data is available, a lot more needs to be done to ensure that a culture of trusting data and being comfortable with relying on insights and decisions made on the basis of that data, is prevalent amongst employees at all levels within the organizations. Only this pervasive trust and reliance on data can drive real benefits of business analytics insights for these organizations.
So, what can CEOs do to build a data-driven culture in the organization?
Align an internal team of data champions — that is, bring in data experts who can manipulate large volumes of complex data.
But make sure that the team includes not just data analysts and data scientists who understand different methods of analysis and can create data models, but business experts as well, who know the mechanics of the business and the organization and can think of ways that data can help the organization overcome key challenges which will impact growth positively.
Bringing together data experts and experienced business managers on the same team has proven to be most effective in terms of ensuring that the data analytics goals outlined in the strategic plan for the data project remain focused on achieving high-impact business goals. Ultimately, the ideal team is one that includes data analysts, business analysts, marketers, sales managers, and operational IT teams to guide how analytics are best applied in order to leverage the real business results.
PWC’s CEO Survey revealed that one of the fastest-growing skills needed in the enterprise is data analytics. How does this fit in with the point discussed above?
While data experts by themselves are not enough for data analytics projects to be successful, many companies may need to hire a new top management layer within the data analytics field. The business landscape has been irrevocably altered and talent in the information architecture and data analytics space is needed to ensure that opportunities to grow sales, enhance revenue, and improve productivity are suitably harnessed.
Several organizations now have a Chief Data Officer, while others rely on the CTO or Head of Analytics to lead this space. CEOs need to ensure that clear directives are given to the data champions team and that recommendations from the team are carefully evaluated and implemented. The data team should then be empowered to deliver actionable insights in ready-to-consume visualizations and live dashboards for end-users.
Make the right investments into AI-powered technology:
Once the right human resources are in place, CEOs also need to make sure that investments into the right technology are made. Think whether your analytics tool currently includes AI capabilities. If not, consider the investment as essential. The reason for this is clear – any investments you make needs to have a long-term perspective; certainly, in the coming years, data is going to explode even further. Traditional tools are not geared for those volumes of data.
Without AI, your data champions may end up spending more time and energy simply preparing data reports rather than using the reports to find solutions to business challenges. Without automated insights, your teams spend many man-hours poring over data reports and still not find that elusive event that can trigger a significant insight and become the source of an important business decision that can lead to growth. With AI/ML, your data champions are relieved of the tedious process of preparing data, creating statistical models and working on visualizations and dashboards. They can instead focus on using augmented analytics and automated insights to take action and implement the decisions that are highlighted by AI.
With several AI-powered tools available today that can yield automated business analytics insights, CEOs will need to evaluate which ones work best for them. Cloud-based, on-prem, or hybrid? Customized, service framework-based, or out-of-the-box applications? Sophisticated tools that are best utilized by data scientists? Or do-it-yourself, self-service, on-demand tools that put the power of data into the hands of every end-user in the organization?