Data, data everywhere, with more than a drop to drink.
This new reality is something of an evolutionary walk as we explore what “data” means to organizations. While collecting data is less than half of the battle these days, taking informed action to further goals is what sets businesses apart.
Let’s start with (what I believe is) an unarguable truism: Data is absolutely everywhere. E-mails, pictures, business transactions, customer profiles, historic sales data, and financial reporting, as well as the onrushing train called the Internet of Things, which includes machinery and equipment diagnostics. All of these sources and more generate streams of data with accelerating growth. Capturing this information in a semi-organized manner is increasingly a resolvable problem, and the cost of data storage continues to fall.
After creating, identifying, and capturing data, the next step for most organizations is understanding what the data means. Knowing where and how to look for business insights amidst this massive amount of data is fundamentally critical.
Welcome to the world of Big Data
Understanding large, historic data sets to gain an idea of what happened over time or during a snapshot in time is one thing. Knowing what is going in real time is entirely different. Given enough time or processing power, it’s possible to comb through incredibly large datasets. Distribute the work among more than a few clients, you can even scan for signs of E.T. However, the real challenges range from “How do you lock and unlock data tables?” to “How do you ensure data integrity?”
Moving data between systems or pulling it out for analysis almost guarantees you’re not able to process information in real time. This is where in-memory computing really starts to shine.
Arguably, this is where digital transformation has its largest impact: Taking action on the insights from Big Data analysis – and doing it in real time. Think of the various enterprise systems and data sources across your organization. Now add in external data sources such as social media sentiment, weather forecasts, and foreign currency exchange rates, for example. The way these data streams are understood and analyzed should yield a business activity or a transaction in the enterprise resource planning (ERP) system.
Now imagine the various decisions you make, based on a variety of inputs, each day. Scale this out: business transactions take place 365 days each year, the number of functional peers you have, the number of geographic teams, other lines of business, and so on. You see where this is going. And any way you slice it, this environment creates a lot of transactions.
The next, perhaps obvious, step here is to apply artificial intelligence and automate routine transactions. This is the current pinnacle of the digital world’s hierarchy of needs. Considering that human intervention, on a per-transaction basis, is expensive, it makes sense to apply it to the nonroutine transactions that add value or require greater insights and intuition than that of a computer. This level of automation is central to unlocking the next wave of productivity, which will transform how business is accomplished in today’s data-driven world.
It’s time to move beyond old-fashioned transaction-only ERP systems. These systems of record won’t support today’s business needs, much less those of tomorrow.