Why Predictive Analytics Has Eclipsed Traditional BI

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Why you need to leverage the power of predictive analytics to update your approach to gaining insight into your enterprise.

Since the dawn of the computer and the corresponding emergence of back-office IT departments, businesses have worked to collect various sources of data and devise ways to mine it, report on it, and extract some form of value from it.

Eventually, technology began to take over as the essential component of modern business and, as cloud infrastructure and the Internet of Things (IoT) became more mainstream, data volumes unsurprisingly started to balloon. So, collecting and deriving value from all this data no longer seemed as straightforward as it had been in the past.

In fact, for many organizations, it became an exceedingly complex and daunting process. Let’s take a look at why I think this has changed the way you need to approach business intelligence for your data strategy.

IoT technology and an evolved market means that traditional BI can no longer cut it   

Business Intelligence (BI) technologies were originally designed to help organizations grapple with ever-increasing data volumes. But, with data continuing to grow at a truly astronomical rate, traditional BI tools are failing to satisfy the complex demands of today’s businesses.

See also: Can predictive analytics fend off the next wave of viruses?

Case in point: technology advancements like IoT have led to both an explosion in the volume of data being collected and used by businesses, as well as a drastic increase in the number of repositories housing this data. As such, data sources have become more fragmented, which has not only reduced the asset value of data but also increased the complexity of mining and manipulating it.

Businesses today are also struggling to use their data effectively as they strive to meet their customers’ demands for more and more data-centric business services. Along with many other challenges, including confusion over data strategy leadership and a lack of overall data visibility, organizations are struggling to execute on even the most basic components of data-driven business transformations.

For instance, in a recent survey of 500 business and IT decision-makers across the UK and Germany, we found that a mere 20 percent could confidently state where all of their critical data resides.

This year, businesses are embracing a data analytics-based approach

In an effort to keep up with infinitely larger and more complex data volumes, today’s most innovative businesses have begun evolving beyond their existing BI technologies and strategies and started embracing a data analytics-based approach instead. In fact, our research indicates that 75 percent of businesses are actively adjusting their data strategy to be more data analytics focused, rather than continuing to maintain their traditional BI systems and processes. 

Data analytics is generally more nuanced than BI. It’s process that includes everything from inspecting, cleaning, transforming and modelling business data to discover useful information, inform conclusions, fuel predictions, and support decision-making.

It also serves as a major driver towards automation and artificial intelligence (AI) in the workplace, as more businesses place data-centricity at the heart of their operations. According to our research, two thirds (62 percent) of organizations claim their move from traditional BI to data analytics is already delivering some of the business value they hoped for. 

Three reasons for the widespread evolution beyond traditional BI 

There are three fundamental reasons why organizations are shifting away from traditional BI and embracing predictive analytics to meet customer demands and stay ahead of competitors:

#1: Legacy databases are creating harmful bottlenecks 

Whether it’s a mainframe, a client server or cloud infrastructure, databases are the repositories of knowledge that power most businesses. But data lakes can act as harmful bottlenecks that curb an organization’s efforts to extract value from data. For instance, as the volume of data has grown, so too has the number of data repositories.

Often due to data lakes’ struggles, data becomes siloed within organizations and fragmented to such a degree that it’s nearly impossible to extract any actionable insights. In fact, more than half (55 percent) of the businesses we surveyed agreed that the fragmentation of data across multiple databases, local storage, and disparate systems is preventing them from fully extracting value from their data.

To overcome database challenges resulting from exponential data growth, businesses should look to unified platforms for collecting and manipulating their data. Additionally, consider implementing a data analytics framework and database that’s fast and flexible enough to work in tandem with legacy and existing systems and provide real-time insights.

Rather than replacing any/all legacy technology or continuing to rely on traditional BI (which depends too much on the centralization of data in a singular data warehouse), it benefits organizations from a range of industries to incorporate unified data analytics platforms that can scale along with their business and help overcome the fragmentation of information and insights across disparate systems, workgroups, and storage platforms. 

#2: Unused data wastes time and money

The unfortunate reality is many organizations aren’t using their data as much as they should. To be clear, it’s not necessarily that businesses are intentionally not using their data. In many cases, they’re simply (unsuccessfully) trying to understand what data they have, which is an understandably monumental task given the high volumes of complex data that every piece of technology is continually pumping out.

In a world where insights-driven businesses are growing at an average of more than 30 percent annually and are on track to earn $1.8 trillion by 2021, it’s concerning that 27 percent of the business leaders we surveyed still don’t understand the value of their data. Success in today’s highly competitive, digital business landscape requires a data-driven approach, and organizations that fail to capitalize on their existing data will not only rack up wasted time and costs, they’ll also lose out to their analytics-focused competitors.

From anticipating customer demands, to keeping shelves stocked, to optimizing internal workflows, data is critical to the daily operations of modern business. As such, allowing data to occupy space without providing any value can be severely detrimental to the health and long-term relevance of any organization. By evolving beyond traditional BI strategies and embracing more modern analytic technologies, businesses can participate in the current ideological shift towards being more data-centric and predictive.

In particular, they should prioritize technologies that make it easy for users to extract actionable insights from data in real or near real-time. Likewise, they can invest in thorough education and training programs to incite cultural change in favor of using and trusting data insights to inform larger business decisions. And it’s important they always ensure there is clear ownership and a concerted company-wide plan for all data strategies.

#3: AI and machine learning (ML) are disrupting every aspect of business

Whether it’s used for streamlining supply chains, stock control, factory automation or repetitive data entry tasks, AI and ML technologies provide invaluable benefits to organizations across a variety of industries. According to our research, 40 percent of businesses believe AI and ML have already transformed parts of their operations, such as customer interactions and workflows, and 41 percent said AI and ML serve as the catalyst that has made data analytics fundamental to their day-to-day business strategy.

That said, to fully reap its benefits, AI and ML technology requires a ready supply of clean, accurate, and detailed data to drive the necessary algorithms. Businesses should therefore use unified data analytics strategies to access the wide range of data collected within their organization to inform any automated decision-making.

After all, asking an AI system to make informed judgements and recommendations when it only has half the information available can quickly undermine the effectiveness of any AI or ML investment. 

If you are thinking of how to do this in your organization, consider implementing dedicated services or tools to ensure your data is of the highest standard and devoid of any anomalies (such as duplication) that could impact AI and ML functionality. Also, prioritize investing in cloud migration and the consolidation of all data sources, as bringing data out of its disparate silos and into fewer repositories is critical to the success of any AI and ML application.

Operational success and long-term relevance requires a data-centric mentality

As demonstrated by recent legislative changes, such as the introduction of the EU’s GDPR and the U.S.’s California Consumer Privacy Act, data holds immense value and must be handled accordingly. When used effectively, data allows organizations to unlock critical insights that can deliver a competitive edge, anticipate customer demand, and overcome any market and/or operational challenges. When used ineffectively, data simply occupies space, costing significant time and money to maintain without returning any measurable value. 

With data and business analytics solidifying its stance as one of the most critical elements in business today, you should take the time to identify the right technology and skills that are necessary for spurring a data-centric mentality within your organization. In particular, make the most of unified, in-memory databases and analytics platforms to ensure a successful data analytics transformation.

And make sure the platform you choose to do this is fast, efficient, compatible and give you access to the information you need. In doing so, you can extract key insights to make better business decisions in real or near real-time. This means you’ll gain a perpetual competitive edge and maximize all market opportunities. 

Mathias Golombek

About Mathias Golombek

Mathias Golombek joined Exasol back in 2004 as software developer, led the database optimization team and became a member of the executive board in 2013. Exasol is Germany-based world leader in analytical database management providing high-performance, in-memory, MPP database specifically designed for in-memory analytics. Although he is primarily responsible for the Exasol technology, his most important role is to build a great environment, where smart people enjoy building such an exciting product. He is never satisfied with 90% solutions and love the simplicity of products. His goal is to encourage responsibility and a company culture that people love to be a part of.

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