Making Sense of our future epicenter: BI & BIG DATA
It’s infiltrating every part of what we do, from driverless cars almost upon us, to our very own digital assistant, from Purchase Prediction to Movie/Music recommendations and much much more. And it’s just huge amounts of data being fed to predict multiple situations and how to react in the future that’s now driving IoT, AI and ML.
Enterprises now look like they’re to be ruled by the data they produce. Previously science fiction, Artificial Intelligence (AI) is infiltrating each day of our lives and business. Algorithms are being created to spot trends amid big data and enable speedier decisions to be made, generally in real-time. And while it’s by no means easy to incorporate, AI is in the end a box containing Math, Rules & Code - “if this happens, then that should take place”
The outcome is an incredible new opportunity (the data-driven economy). Today, business uses less than 1% of all data collected. It’s about monetization that offers the greatest potential value, through the analyzing of new and existing data to identifying unrecognized revenue streams and strategy.
So what’s it all about?
Analytics, Big Data, Data Mining, Data Science, Predictive Analytics????
So now let’s look at the multiple terms such as Analytics, Big Data, Data Mining, Data Science, and Predictive Analytics, all floating around in the space of Business Intelligence and how it makes a difference to the consumer and business world.
Now not everyone will be fully in agreement with my definitions, but here goes…
As the name suggests it’s a huge amount of internal and external data coming from research, manufacturing, processing, customers, suppliers, sales etc that’s then processed and stored both on-premise on databases and, or in the cloud. Coming from multiple angles and layers, it’s frequently unstructured data and therefore takes time and expertise to decipher.
While historically we have monitored care of KPIs and CSFs, today BigData provides almost limitless data to work through for greater Business Intelligence. Big Data is the library you visit when the information to answer your questions isn’t readily at hand or seeking answers to questions you didn’t even know you had.
With so much data, it’s hard to know you’re not missing something that could make a business impact and therefore Data Mining is the start of seeking out those new pieces of intel.
Data Mining is deciphering the evidence in search of previously unrecognized patterns. The larger companies hire Data Scientists who are statistics and computer science experts who know all the tricks for finding the signals hidden in the noise.
There is much debate with regards to whether Data Science is different to that of Analytics, however recently I came across the following: “Data Science is the discipline of using quantitative methods from statistics and mathematics along with technology (computers and software) to develop algorithms designed to discover patterns, predict outcomes, and find optimal solutions to complex problems”. Ultimately, it can help support and even automate the analysis of of mined data. For me, that’s very different to that of….
A case of taking what is there and slicing the data; assessing trends, comparing one to another, seeing trends and visualizing it intuitively at a glance. In essence it looks at historical data.
Ultimately, analytics is about asking questions: How did a promotion perform one month, versus the previous month? How is one geography doing compared to another? Analytics is therefore generally the data crunching, question-answering phase leading up to the decision-making phase in the overall Business Intelligence process.
You need predictive analytics to optimize your resources as you look to make decisions and take actions for the future. Predictive analytics, on the other hand, builds analytic models at the lowest levels of the business—at the individual customer, product, campaign, store, and device levels—and looks for predictable behaviors, propensities, and business rules (as can be expressed by an analytic or mathematical formula) that can be used to predict the likelihood of certain behaviors and actions.
Predictive analytics is about finding and quantifying hidden patterns in the data using complex mathematical models that can be used to predict future outcomes.
This is the broadest category and encompasses the other terms already detailed. BI is essentially data-driven decision-making. Organizing this data, creating and testing algorithms is of course where many companies come unstuck!
But as AI has gained momentum, big application providers have gone beyond creating traditional software to developing more holistic platforms and solutions that better automate business intelligence and predictive analytics processes. Those big providers of software are creating digital innovation suites that combine this data, fast processors, and amazingly thorough algorithms to create the new age of AI & ML.
And, none more so than the enterprise software giant, SAP with its Leonardo…..
SAP’s Leonardo suggests taking its huge customer data and processes experience and applying it in the near-term as Applied Data Science as a Service
It appears with less than 1% of data analyzed, we are at a stage of Unconscious Incompetence - unaware of what we don’t know and therefore enterprise goals revolve around:
finding out what we don’t know
understanding our ever evolving and new customers
and recognizing data is a new “Asset”
By pulling together relevant data analytics technologies and coupling them with relevant business-related data as a packaged, marketable entity, SAP is looking to short-circuit the process of companies innovating their own new ways of doing business and frankly who can blame them with so many industry learnings and best practices under their belt?
For companies to really exploit their data monetarily, technology is the vital ingredient. it’s not just the scale of the data, but its ever increasing velocity, variety, veracity and volatility!
The traditional infrastructure of data warehouses are unable to handle these demands, never mind monetization. However, there are now various technologies that are here to exploit the landscape, sitting across the networks, data hubs, blockchain and application program interfaces with machine learning. SAP has invested significantly in both acquisitions and R&D to provide and entire closed-loop solutions for data monetization.
But of course success in the data economy comes from much more than the adoption of new technologies, in so much that it demands change on a far wider scale. As is so often the case it relies on a customer-centric approach that combines Strategy for Data Monetization, its Culture, People & Processes, Winning as an Ecosystem, Technology and AI & Automation.
SAP’s DataScience-As-A-Service appears today as the most comprehensive cloud offering that allows its customers to leverage the full potential of data to deliver on their data-monetization strategy.
There are however key learnings from their experiences so far:
monetizing data is a new category and it needs a holistic approach
data is the asset of our time and be strategically viewed to other company assets driving growth
success is not about the technology, but abut the winning strategy with data at the epicenter
cybersecurity and data privacy are vital in leveraging the full potential
it will require new and different skills to exploit
speed is critical and early wins required to show the business tangible results
The ocean of Big Data is not that exciting admittedly, however navigating through to the discovery of hidden treasures certainly is.
Next week, we move onto the incredibly exciting AI & ML, but in the meantime if you need BI Resources or generally across ERP3, please let me know as that’s where you’ll find me “bouncing off the ceiling”