Here’s the thing about digital transformation: It’s all about data and what you do with it.
Yes, much of the conversation is about cool emerging technologies such as the Internet of Things (IoT), machine learning, and blockchain. But in the end, all of these technologies are simply ways of making better use of data.
An IoT deployment uses sensors to transmit machine data to home base for analysis. Machine-learning algorithms use data inputs to help improve processes. And a blockchain, by definition, is a kind of secure, transparent, and distributed database of transactions.
So when it comes to digital transformation, the starting point is to think about ways in which to put your data to better use – and into the hands of those who need it to drive business performance.
From data to analysis
The true value of your data comes from analysis – the kind of insight that drives innovation and helps you deliver what your business needs. This is why companies invest so heavily in business intelligence and data analytics solutions.
The challenge is for organizations to increase the penetration of analytics within the organization and empower more business users to make better, fact-based decisions. The smarter, more timely decisions you can make, the stronger your business will grow.
Research by Oxford Economics from 2016 bears out this thinking. Oxford surveyed more than 2,050 executives and 2,050 non-executive employees in 21 countries, across multiple industries. For digital leaders, the research shows that:
- 78% of decisions are data-driven
- 62% of decisions are made in real time
- 62% of decisions are distributed across the organization
But for other companies, what is it that stands in the way of wider use of analytics? One obstacle is the fact that data analytics historically has been an IT specialty requiring technical know-how. More recently this has changed. Today’s data analytics market offers a wide range of self-service solutions that allow end users to do sophisticated analytics on their own.
This is a good thing. But even with the rise of self-service analytics, businesses are still experiencing low adoption rates. Why? I think it has a lot to do with on-premise deployment and the solutions that have been available to them. Companies have invested tremendous amounts in data analytics solutions – but the vast majority of these solutions have been implemented behind the enterprise firewall.
Toward the cloud option
It’s hardly a news flash that cloud deployment can provide you with the potential for flexibility and greater reach. Still, many companies operate from a mindset of wanting to get the most out of their existing investment. This is to be expected. After all, maximizing this investment makes logical business sense.
But on the other hand, data analytics in the cloud can help you expand user adoption – which is a powerful incentive to move forward. For example, with cloud-based, self-service data analytics, you could centralize all reports and make them available enterprise-wide. You could also allow users to rate reports so that the most useful float to the top and help fuel greater adoption.
Or take the example of a larger auto manufacturer coordinating with its dealership network. With the cloud, manufacturers can make available and accessible critical program data on inventory, pricing, incentives, and much more. Doing the same thing with systems at dealerships connecting to systems at the manufacturer would require a lot of time, money, and energy to deploy and maintain. For increasing analytics adoption to a wider base of stakeholders, the cloud is easier, faster, and more cost-effective.
Extend and expand with hybrid analytics
But still, a wholesale move to the cloud just isn’t in the cards for some companies that have invested in on-premise data analytics and need to realize the value of that investment. This is where hybrid analytics comes into play.
Think of hybrid analytics as the first step on your journey to the cloud – one that allows you to extend and expand your on-premise data and make it available for cloud consumption.
Hybrid analytics can be seen as a variety of bimodal IT. Here the notion is one of two lanes for IT: one lane dedicated to a “system of record” (your core data investment), the other dedicated to a system of innovation (rapidly built line-of-business solutions to capitalize on opportunities). These two lanes exist side by side, with the system of innovation leveraging the system of record to do new things in the digital economy.
The fact is, end users typically are not concerned with where the data lives. They just want access. And if your goal is to get more value out of your data, providing access to it for high-end analytics is a good first step forward. As you move forward with hybrid analytics, you can evaluate the value of the cloud deployment at a more fundamental level. Does it make sense for you to switch over to an all-cloud all-the-time model? Maybe, or maybe not. But with the experience of hybrid analytics under your belt, you’ll be in a better position to make an informed decision for the benefit of your company and your customers.
Originally published on Nov 13, 2017 by Steve McHugh in The D!gitalist magazine online. Steve McHugh is Director for BI Enterprise Marketing at SAP with a solid background in enterprise performance management and analytics.
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