Analytics for all - not just Rockefellers, rock stars and rocket scientists

Profile picture for user Pedro Arellano By Pedro Arellano January 29, 2019
A new generation of advanced analytics is accessible to all, not just those with money, status or special skills, writes Pedro Arellano from Birst

Four people's hands holding mobile devices showing analytics by rawpixel on Unsplash

The elite often enjoy certain luxuries and privileges, from premium seats to rock-star pampering and platinum-level perks. In business, the classic divide between technology haves and have-nots can be illustrated this way — those who have advanced analytics in their IT arsenal, and those who don’t. Typically, only the large enterprises with armies of data scientists, business analysts and big budgets have enjoyed the benefits of future-seeing analytics and business intelligence (BI). For many other companies, spreadsheets, hunches, and a large degree of luck have often been the great enablers.

Until now.

Advanced analytics and business intelligence are now more accessible than ever before. There are user interfaces now available that step managers through each process of defining problems, teaching the system what variables to examine, applying Machine Learning algorithms, and testing the algorithms. The old 'barriers to entry' for such technology haven’t just been removed — they’ve been torn down and pulverized.

Looking beyond the rearview mirror

The previous generation of analytics and BI was all about taking a slice of data and producing pretty charts and dashboards. Line of business managers had to knock on the IT department’s door, then get in line to request specialized reports. To look deeper into influencing factors behind performance anomalies or unexpected trends, managers often had to call in the big guns.

With a shortage of skilled IT experts, many companies struggled to hire personnel with expertise in Business Intelligence or AI, forcing companies to turn to third-party consultants. The price tag created additional budget hurdles to overcome, for some. Skilled data scientists who could apply behind-the-scenes magic and extract meaningful conclusions from mountains of data were much like unicorns —  rare, elusive and mysterious.

The next chapter in analytics

Turn the page, and there’s a new chapter in analytics. This time, instead of only the companies with deep pockets and stellar talent having access to the land of insightful analytics, the entire kingdom is invited. New solutions have helped to level the landscape, giving mid-size organizations as well as start-ups the ability to tap into the valuable power of data. Today, advanced analytics can perform the 'heavy lifting' in the back-end, connecting, preparing, and relating data from a variety of disparate sources across the enterprise. Augmented analytics apply the insights of Artificial Intelligence, giving users advice and recommendations. Actions are recommended, based on the data.

Then, thanks to Machine Learning, the system learns and continues to refine its algorithms, based on the user’s reaction to the recommendations. When recommendations are followed, the system learns that the equation is right. If the user, rejects the advice, the system learns to no longer make that recommendation. The BI solution continues to improve and refine over time, as it is exposed to more data points.

The applications of these new techniques are only beginning to be fully comprehended. As more and more organizations experiment with proof-of-concept projects, more use-cases and best practices will be defined. Now, the specific applications seem to be as varied as the types of companies and uniqueness of their products and services. That is the beauty of this current technology. It is highly flexible, easily tailored, and it can evolve as the company’s needs change.

Flexibility supports changing needs

With the rapid pace of change today, managers need solutions that they can use, reuse, and reuse again in different situations and applications. The KPIs of interest today may be obsolete tomorrow. New product introductions may depend on different data sets. Expansion plans may call for analysis of new markets, sales channels, or under-serviced demographics. Managers must be able to 'teach' and guide the BI solutions toward new directions — quickly and easily. There is no time or budget for re-writing code or starting from a blank slate and building a report that takes a rocket scientist to interpret.

The benefits of easy-to-use augmented analytics are simple — growth and more growth. In its report, Critical Capabilities for Analytics and Business Intelligence Platforms, published in May 2018, Gartner wrote:

By 2020, the number of users of modern business intelligence and analytics platforms that are differentiated by augmented data discovery capabilities will grow at twice the rate — and deliver twice the business value — of those that are not.

Natural language processing (NLP). This important capability allows the system to interpret or produce human-readable text or spoken audio, making it easier for individuals at all levels in the organization to engage with analytics — without advanced training. The replacement of traditional reports with smart narratives has the potential to dramatically expand the use of analytics.

Semantic layer. This is a common set of business definitions across analytic instances and it gives users the ability to define business rules that can be leveraged by AI-enabled systems, and apply specific terms to operational process. This is a critical element that makes it possible for business users without analytics expertise to work with data.

Visualization tools. The dashboards generated should help visualize results and prioritize the questions based on potential impact.

Contextual relationships. By selecting what context is considered relevant, the user controls the variables and defines personalized priorities, rather than falling back on pre-built standard performance measurements.

Contributing factors. Business users can also bring in data about geographic location, environment, weather, suppliers, and product specifications, which might impact the asset’s performance. Applying insights across an extended data set can lead to a more complete picture the enterprise’s current state.

Machine Learning. Built-in Machine Learning capabilities improve accuracy over time. The ability to expand its knowledge-base and 'learn' the user’s expectations make the solution more and more valuable.

Next-step take-aways

Enterprises today have many challenges — and many opportunities. Making well-informed decisions is time consuming. In the past, companies were limited in their use of advanced analytics. Data overload was common. Now, companies of all sizes can benefit from easy-to-use augmented analytics, which leverage AI and Machine Learning capabilities.

This extra layer of insight will help managers make critical decisions quickly and easily, speeding response time to volatile global trends. In today’s fast-paced world, agility is essential. Modern augmented analytics help managers make bold moves with confidence — just like a rock star.