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.
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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.
This year, businesses are embracing a data analytics-based approach
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.
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.
Three reasons for the widespread evolution beyond traditional BI
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.
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
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.
#2: Unused data wastes time and money
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.
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
#3: AI and machine learning (ML) are disrupting every aspect of business
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
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
After all, asking an AI system to make informed
Operational success and long-term relevance requires a data-centric mentality
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.
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.
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