What is the value of data in the age of big data?

Big data’s value is found in management and mining that meets business strategy

By Gavin Morrison, MD of Cubic Blue, a Knowledge Integration Dynamics company

Why are so many companies running big data programmes to get big data analytics? Because they see the value. Or they realise there’s potential even if they don’t know what it is yet. The 2014 IDG Enterprise Big Data research found:

  • Businesses expect a 76% increase in the total volume of managed data in 12 to 18 months
  • 49% of businesses back then were already implementing big data projects
  • Enterprises were ahead of SMBs

SAP conducted its own study in August 2014. It made some interesting findings:

  • 82% of respondents believe big data analytics will help them meet strategic objectives
  • 88% believe they must be able to share big data insights across lines of business

The way we work is changing and talent retention is going to become increasingly crucial. Data will help managers do that with HR analytics mining personnel data. For example, a healthcare company abroad found that it paid variable rates to people doing the same job. They were unhappy as they realised or suspected this was the case and turnover was consequently higher. So the company mined the data to figure out maximum and minimum pay thresholds. Employees were happier thereafter and reduced attrition. It also saved the company a bundle.

LinkedIn Visual Map
Data by itself is pointless – you need to make sense of the volumes you have. This visual LinkedIn map can be overwhelming if you don’t filter the data with purpose.

Image Credit: Luc Legay

Financial investment and management companies also use data to figure out where, why and how to invest client funds – shifting away from gut instinct to fact-based investment decisions to deliver more competitive returns. Statistical and computational improvements Successful data programmes improve statistical and computational methods so that companies can do more with data rather than just collect a lot of it. Certainly there’s a lot more available and once the Internet of Things (IoT) kicks up a gear or two that will grow. The data centre, data management, and data warehousing are all important disciplines that cannot be neglected. They drive robust analytics and BI, which is where the real value is generated. Those, not the infrastructure or the raw zettabytes of data, help businesses meet their strategic objectives such as:

  • Talent retention necessary to be competitive in future
  • Reduce personnel costs by understanding market- and geography-related pay
  • Successfully enter new markets by conducting better (more informed, fact-based) cost and market analyses

Those are just three examples off the top of my head and every business will be able to come up with hundreds of examples, particularly as the executives begin to drill into the capabilities of properly mining, analysing and massaging the data. The real value of data in the age of big data is being able to make sense of it – not collect big volumes of the stuff. Tie that back to what the business needs, what it’s trying to achieve, and you’ll have the same winning formula that so many companies are tripping over themselves to get.

The wielder, not the axe, propel plunder aplenty.

By Mervyn Mooi, Director at Knowledge Integration Dynamics. (KID).
Johannesburg, 25 Sept 2014

Business intelligence is a fairly hot topic today – good news for me and my ilk – but that doesn’t mean everything about it is new and exciting. The rise and rise of BI has seen a maturation of the technologies, derived from a sweeping round of acquisitions and consolidations in the industry just a few years ago, that have created something of a standardisation of tools.

We have dashboards and scorecards, data warehouses and all the old Scandinavian-sounding LAPs: ROLAP, MOLAP, OLAP and possibly a Ragnar Lothbrok or two. And, like the Vikings knew, without some means to differentiate, everyone in the industry becomes a me-too, which means that’s what their customers ultimately get. And that makes it very hard to win battles.

Building new frameworks around tools to achieve some sense of differentiation achieves just that: only a sense of differentiation. In fact, even when it comes to measurements, most measures, indicators and references in BI today are calculated in a common manner across businesses. They typically use financial measures, such as monthly revenues, costs, interest and so on. The real difference, however, comes in preparing the data and the rules that are applied to the function.


A basic example that illustrates the point: let’s say the Vikings want to invade England and make off with some loot. Before they can embark on their journey of conquest they need to ascertain a few facts. Do they have enough men to defeat the forces in England? Do they have enough ships to get them there? Do they know how to navigate the ocean? Are their ships capable of safely crossing? Can they carry enough stores to see them through the campaign or will they need to raid settlements for food when they arrive? Would those settlements be available to them? How much booty are they likely to capture? Can they carry it all home? Will it be enough to warrant the cost of the expedition?

The simple answer was that the first time they set sail they had absolutely no idea because they had no data. It was massively risky of the type that most organisations aim to avoid these days. So before they could even begin to analyse the pros and cons they had to get at the raw data itself. And that’s the same issue that most organisations have today. They need the raw data but they don’t need it, in the Viking context, from travellers and mystics, spirits and whispers carried on the wind. It must be good quality data derived from reliable sources and a good geographic cross-section. And in preparing their facts, checking they are correct, that they come from reliable sources, that there has been case of broken telephone, that businesses will truly make a difference. Information is king in war because it allows a much smaller force to figure out where to maximise its impact upon a potentially much larger enemy. The same is true in business today.

Before the Vikings could begin to loot and pillage they had to know where they could put ashore quickly to effect a surprise raid with overwhelming odds in their favour. In business you could say that you need to know the basic facts before you drill down for the nuggets that await.

The first Viking raids grew to become larger as the information the Vikings had about England grew. Pretty soon they had banded their tribes or groups together, shared their knowledge and were working toward a common goal: getting rich by looting England. In business, too, divisions, units or operating companies may individually gain knowledge that it makes sense to share with the rest to work toward the most sought-after plunder: the overall business strategy.

Because the tools and technologies supply common functionality and businesses or implementers can put them together in fairly standard approaches as they choose, the real differentiator for BI is the data itself and how the data is prepared – what rules are applied to it before it enters the BI systems. Preparation is king.

These rules ultimately differentiate information based on wind-carried whispers or reliable reports