For most businesses, extracting value from data can be daunting. Businesses accumulate vast amounts of information, but that data isn’t always organized, accessible or even usable. Data managed poorly can mask a wealth of transformational business insights, and companies must find a way to extract these insights and use them to drive forward strategically.
Until recently, deriving value from disparate data has required highly skilled data scientists or data analytics specialists with professional qualifications. But these people aren’t cheap to hire and can sometimes be disconnected from the burning issues that drive the business, as well as what kind of information would make a competitive difference.
From Convenient to Critical
Quality data insight is no longer a luxury. Any business looking to drive transformational change must have teams that understand both the business and what it takes to pull the right predictive and prescriptive insights. Thanks to the evolution of technology, we’re now seeing a wave of smarter, more accessible data systems that can be deployed by any organization without the need for specialist qualifications or code.
The learning curve with the latest enterprise tools is much less steep than with previous generations. Capabilities like self-service drag-and-drop simplicity, code-free automation, along with built-in help and extensive community support all make the road much easier to navigate. The data journey has never been shorter and, as companies strive to become more efficient during difficult times, there is ample motivation to embrace smart, data-driven decision-making.
Coca-Cola is a company that understands the importance of streamlining and efficiency more than most. Its Freestyle touch screen soda fountains with 165 different Coca-Cola drink products that can be mied and dispensed indivually with custom flavors are found in a number of retaurants. Clearly, this requires the ability to dispatch a large number of ingredients to vendors on short notice — and choosing the right volumes used to be tricky, often leading to waste.Coca-Cola has fixed the problem with data analytics. Using telemetry, the machines send data back to the firm about what is selling well, and when. Coca-Cola is then able to compare those findings against a host of other variables tocreatepredictive models that anticipate demand to balance inventory.
In Asia Pacific, the Salvation Army, one of the world’s largest social welfare organisation with more than 1.6 million members in over 120 countries, automated their data migration in an easy and repeatable way. With over 200,000 rows of data, they were able to process, prep and move data from disparate systems, blending them together and generating a savings of over 2,000 hours of manual effort.
Thanks to next generation of self-service analytic platforms, these complex but valuable processes are no longer reserved for the “big boys.”
A Holistic Approach
Soon, having one centralized platform for all analytics assets will be as essential to business as having a customer relationship management (CRM) database or accounting software. A good data platform is essential for capitalizing on the data economy because it supports analytics creation and consumption across an entire organization. By democratizing data access and discovery, every worker will be empowered to ask the right, business-relevant questions and obtain swift answers without relying on highly trained data professionals.
Additionally, and perhaps most importantly, shareable analytics platforms will be critical to unifying the data, analytic processes and people within an organization. By making all data work additive, and relevant insights seamlessly (and securely) accessable by relevant parts of the organization, every business will fast-track its digital transformation journey.
Too often the value of data is lost due to organizational silos, a clumsy patchwork of tools and a limited number of “data owners” within the business. Today, a common point of entry is crucial so that insight can be converted to action in the shortest possible time.
The new category of Analytic Process Automation (APA) software is swiftly differentiating itself by accelerating the rate at which businesses can make critical, data-driven decisions. These systems automate everyday processes and guide any user through the entire data continuum – from data discovery, to insight, to action.
One day, we may reflect upon intelligent analytics software as an early example of enterprise automation that got it right. We may ask ourselves how we ever lived without systems that so readily opened access to data findings. Technology excels when it simplifies complexity. It allows business leaders to free-up staff for more cognitive and creative tasks.
We stand on the precipice of a significant evolution in how businesses use data. To move forward, organizations must push beyond descriptive and prescriptive analytics.
Successful businesses like Amazon and Netflix continue to stand apart from competitors by making accurate predictions about tomorrow, too. Given the complexity of building predictive models with code, most businesses have not yet realized the incredible, accelerative advantages of predictive analytics. An “analytic divide” is emerging where companies with access to analytics and automation skills are leaving those who lack those capabilities behind.
APA systems are here to change that. Through these platforms, methods that have required a high level of skill previously can now be executed by employees throughout an organization thanks to ‘code-free’ and assisted building blocks that can construct models with transparency and make data science learning and upskilling easier.
Companies do not need to hire specialists for this role. As more businesses embrace prediction, entire industries will reinvent themselves.
The Data Problem
Sometimes the greatest obstacle is the data itself. In business we’re surrounded by it and yet it can often be difficult to understand. In many cases, the most valuable data – data that can drive the best, most precise predictive insights – is buried in PDFs or images, or perhaps even in a more abstract form, like the opinions or emotions of customers.
Fortunately, the world around us can now almost entirely be understood in terms of quantifiable data. Be it a photograph, a piece of text or even a handwritten note. Intelligent software allows us to lift information from its original context, and this capability is now being democratized so that any business can use it to inform fresh perspectives on stubborn problems. Again, a user-friendly format is already liberating these capabilities from the grip of data specialists and handing any business what it needs to import difficult data.
The Future Is Bright
This level of dexterity is beyond anything we’ve seen from legacy enterprise systems. It puts the power of discovery and prediction directly into the hands of decision-makers, expediting what has often been a slow, tedious and skills-dependent process.
Today, there is much speculation about the future of the workplace, not all of it optimistic. Yet, this is also a moment to look ahead to data – coupled with automation and intelligence – helping to drive real, measurable business outcomes while amplifying the thinking of the human at the helm. If this happened at scale, we could expect a resurgence of smart, responsive organizations equipped to tackle whatever comes next.
We already know that many businesses are sitting on a goldmine of intel and untapped talent. The next generation analytics platform will have less to do with what technology and automation will take away, but about what it will give back.