Towards the New Data Landscape

Publish date:

Guess we all get it by now: data is a big thing. A whole wave of breakthroughs in technology takes away limitations on how much data we can store (it’s unlimited), on how structured it must be to analyze (anything goes) and how long we must wait for data to become valuable insights that we […]

Guess we all get it by now: data is a big thing. A whole wave of breakthroughs in technology takes away limitations on how much data we can store (it’s unlimited), on how structured it must be to analyze (anything goes) and how long we must wait for data to become valuable insights that we can take action on (how about right now). It creates a new benchmark for the enterprise data landscape in terms of cost, manageability and agility. But just as much it redefines the way we look at key business activities, whether they pertain at the customer experience, internal operations or even creating entirely new business models.
Disruption is looming around the corner indeed, as stipulated by our most recent report Big & Fast Data: The Rise Of Insight-driven Business. A majority of the companies interviewed believe that the new data landscape will thoroughly shake up business as we know it, not in the least by new entrants that use data to create brilliant, intelligent products and services and equally superior delivery.
Luckily, enterprises still have the option to be on the sending or receiving end of disruption. As we recently stipulated in our TechnoVision 2015 perspectives, it’s not all about ending up in the Valley Of Doom. By embracing data – living and breathing it – enterprises can take the lead in becoming truly insightful, making much better use of the assets they possess and thus outsmarting the competition.
It takes more than turning a switch though, to become an insight-driven enterprise. It’s a journey. In our report, we suggest 7 guiding principles that will help getting there. Rest assured we will elaborate on each and every one of them in much more detail in the months to come. Just a few highlights here to give you a flavor of what is coming. Stay tuned!
1. Embark on an insights journey within your business and technology context
Data and insights should be pivotal to your overarching digital vision. If you don’t have such a vision yet, you’d better start there. Also, there is no substitute for measuring your baseline: an assessment of both the current state of your data landscape – and the way insights are currently being used for business – is a necessity to get the journey started. Ever occurred to you that insights are needed to become successful with insights?

2. Enable your data landscape for the flood from connected people and connected things
As said, the essence of the new data landscape is that there are no more limitations to volume, structure and timeliness. You need to transform your current data estate in a stepwise way, taking advantage of new technologies to – first of all – considerably save costs and management attention but also to prepare for whatever needs the insight-driven enterprise may have to fulfill its ambitions.

3. Create a data science and analytics culture
Or to put it differently: you need an Insights Everywhere mindset. In order to make insights the centerpiece of transformation, everybody – from the boardroom to the shop floor – needs to become a bit of a data scientist. Not necessarily in the Alan Turing sense of the word, but definitely with a commonly shared fascination for the way data can be turned into value.

4. Unleash data and insights as-a-service
Using data and insights is key, not producing them. At least, it should be to most of your business users. At the heart of platform thinking is making digital assets – including insights – available through simple APIs that can be integrated into any solution, workflow or user interface. All the insights, but not the hassle; that’s what we need.

5. Make insight-driven value a crucial business KPI
Oh, did we already mention that you need insights to become successful with insights? If you want to convince the business side of the value of real-time, gorgeously presented performance information, you may want to start with using these dashboards yourself to demonstrate how data creates business value. Drink your own champagne, as we like to say in our company.

6. Master the governance, security and privacy of your data assets
Security and privacy have proven themselves as undisputed passion killers for any next generation data initiative. And rightfully so, as there is a delicate balance between leveraging data for better products and service and the creepy zone of losing data or using it in the wrong way. The secret is in not only getting the basics right of the Maslow pyramid of data (being safe, compliant and in control) but actually providing a powerful foundation for trustworthy business to the enterprise, enabling new business rather than preventing it. Recent developments such as the emerging Consumer Engagement Principles should be carefully followed in this context.

7. Empower your people with insights at the point of action
Sure, there’s a reason why actionable insights are often desired, yet so rarely achieved. Using insights at the point of action requires a deep understanding of people, the way they work and how insights could not only improve their results – but also fundamentally change the way their business is done. It also requires tightly knit teams that unify disciplines such as business analysis, data science, solutions development, testing, and infrastructure operations to continuously provide better and better insights to wherever it is needed.

Related Posts

Insights & Data

Gesture recognition for a safer, more inclusive society

Date icon August 12, 2021

The emergence of hot tech: Gesture control and touchless user interfaces ~ for a low-touch,...

Insights & Data

Time to shift the gear with software defined vehicles

Date icon August 5, 2021

Software and Data Drive Change in the Automotive Industry.


Beyond the AIOps hype: Part 2

Sindhu Bhaskaran
Date icon August 7, 2020

In this, the second in her blog series exploring AI for IT operations (AIOps), artificial...