Big Data: Turning Data into Knowledge and Putting Knowledge to Work

Originally published April 15, 2011

You probably suspect that your current data overload is going to get worse (and you’re right). One reason the flood of data is only going to rise is because of you: If you’re a C-level executive, the chances are good that you’re not only tolerating the proliferation of “big data,” but are demanding it.

Executives we’ve surveyed tell us they have an insatiable desire for more data. They and their companies appear to be addicted. And with reason: Executives believe that information will fundamentally change their businesses.

It may – and it may not. Whether executives can ride the wave of big data to a more successful business depends on how they use their data. A key part of this is understanding where your company sits on the data maturity model and being able to leverage business intelligence in the right way.

Data by itself is useless, especially for improved decision making. Companies that don’t realize this try to improve their odds of success by acquiring yet more data. Instead, they should realize that data, like iron ore, is merely a raw material. Smart companies use business intelligence to process their data so that it turns into information and, ultimately, into knowledge.

Knowledge is the true destination in the pursuit of data. When the enterprise turns its data into knowledge, it has tools with which to pursue and gain competitive advantage, and even build entirely new business models.

Putting Knowledge to Work

With an infrastructure designed to turn data into information and, ultimately, knowledge, the enterprise is better positioned to respond and innovate in all phases of its operations,such as customer relations.

The technology now exists to develop a 360-degree view of a company’s customers, including their demographics, economics, and preferences. To gain this view, the company gathers data not only from its own databases, but also from customer communications, social networks, blog comments and other channels.

By mining this data for information, the company gains the insight to understand customer needs and predict customer behavior. The company also can suggest additional products or services to its customers – a great way to boost both customer relations and revenues.

Companies that push knowledge further can use it not just as an enhancing factor, but as their business model. Well-known companies in this category include Amazon and Netflix. Their analyses and projections of customer behavior are at the core of their business models, and have propelled them to success.

The Data Maturity Model

Amazon and Netflix are great examples of companies that are far along on the maturity model for data. This maturity model addresses the issue of big data, and how companies can manage their data in increasingly strategic ways, turning that data into information and, eventually, knowledge. Below are the five maturity stages and a quick overview so that you can determine where your company fits in this spectrum.

Stage One: No Usable Data

The theoretical base of this model is the company with little or no useful data. At this level, the company can’t run metrics, and doesn’t fully understand, let alone anticipate, customer needs. It has no useful, information-backed insights with which it can better run its business.

But don’t worry. If you’re reading this, you’re not at this stage. In a competitive market, companies at stage one don’t exist – at least not for long.

Stage Two: Big Data

Companies at this stage are inundated with big data. They have a steady flow of data from both internal and external sources, but few have the tools needed to turn their data into information.

Employees spend more time looking for information than analyzing. In many instances, employees give up, swamped by the flood of data. They make their decisions on the basis of little or no information. And their companies never get the chance to turn their information into strategic and competitive assets.

For newly founded companies seeking to create their initial data infrastructures, a first step would be to identify the data sources – both internal and external – of relevance to them. Then, they should put mechanisms to capture that data in place. They’ll then be in a position to build the data structures that will enable at least rudimentary analysis. From this point, they’ll also be in a position to move up the scale of maturity.

Stage Three: The Right Data

Stage three companies use high-quality data, and apply both context and relevance to their data models. They have built corporate taxonomies and metadata that help  categorize and explain data in meaningful ways, as well as explain the relationships and interdependencies among the data.

A key part of achieving stage three is implementing a cultural shift within the organization that parallels the technological shift. This means content consumers also have to accept responsibility for being content creators. They must provide the data that’s expected of them, when it’s expected of them, in the taxonomies and with the metadata that the company has specified.

Stage Four: Predictions

Companies in stage four can do more than conduct historical or retroactive analysis – they can also conduct predictive analysis. By knowing what is likely to happen tomorrow and beyond, companies at this stage can predict customer behavior and market demand.

For example, major pharmaceutical companies use predictive analysis to boost manufacturing efficiency and quality assurance. Automotive companies can use predictive analysis to increase the quality of customer service.

Stage Five: Strategy

In stage five a company’s entire business model is built around its analytical models.

Getting to stage five requires the development of predictive models that operate quite differently from the historical analysis in which the company has engaged up to this point. But historical analysis techniques – such as data mining – remain important for the ways in which they can inform the more forward-looking analysis that must be done.

Furthermore, predictive analysis must not be an afterthought, or a process that takes place only after some milestone or period of time has passed. Instead, predictive analysis must be integrated into core business processes so that risks and opportunities can be identified and acted upon earlier than would otherwise be possible.

The Next Step

Unless you’re one of the very few companies who have mastered knowledge management, there’s still progress to be had, no matter where you are on the maturity model. The first step is to evaluate your status and to begin to plan for the future.

  • Markus SprengerMarkus Sprenger

    Markus is a BI Global Solutions Director and Avanade's primary business intelligence (BI) expert. He defines and directs the implementation of Avanade's solution strategy relative to Microsoft's BI products and alliances, including the creation of intellectual property (IP) and reusable implementation assets that accelerate customer deployment.

    Markus leads a team of solution architects who work closely with the Microsoft Office Business Applications product group. His team influences the Microsoft product road map through the escalation of technical learnings, challenges and feedback from Avanade customers and the global BI community. Markus joined Avanade in 2005, and has more than 10 years of experience in designing and delivering Microsoft-based BI solutions. Prior to joining Avanade, he owned a business intelligence consulting company in Germany and worked in the management of a BI-focused ISV.

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