Gartner’s report "Big Data Strategy Components: Business Essentials" talks about the necessity for business leaders to be intimately involved in developing "big data" strategies and investigates several business related strategy components that are crucial to success. I hope this summary comes in useful.

The challenges in developing big data strategy components are that almost no organizations have an articulated strategy for big data. Big data initiatives are unique, not only in terms of technology, but also from a business and organizational perspective. Many big data initiatives originate with business units leaving IT in their wake or ill-prepared to adequately support them.

According to Gartner, big data initiatives will bring about significant business, organizational, technological and, in some instances, industry transformations. It is important to recognize that big data initiatives are different from other business and IT initiatives in a variety of ways. Among some progressive, nimble and risk-embracing organizations, big data initiatives are already paying handsome rewards.

From early predictors that enable unprecedented agility, to targeted personalization that generates goodwill and revenue boosts, to previously impossible product and process innovations, big data has proven it can be a game changer for enterprises and industries.

Big data initiatives are all about change — changing business processes, data sources, infrastructure, architecture, skills, organizational structures and economics. They often result, not in incremental improvements to existing business practices, but in radical changes to existing methods or even their outright displacement.

Compared to other projects, big data plans tend to concentrate on acquiring, integrating and preparing information rather than the data's functionality - which may be as straightforward as identifying correlations, anomalies or patterns. This shift in focus can strain traditional approaches to enterprise architecture, project management and role definition.

In order to be successful, Gartner recommends:

Identify potentially valuable data sources

Start scouring for relevant big data sources from "dark" underutilized data, but also consider external commercial and public data service providers and social media. Many great ideas for big data initiatives come from an understanding of the range of data sources available and what questions can be answered if they were integrated and correlated.

Gartner identifies five distinct types of sources:

1. Operational data: Information about customers, suppliers, partners and employees that is readily accessible in online transaction processing and/or online analytical processing databases. It typically includes transactional data, contact data and master data.

2. Dark data: Information collected during the course of business that remains in archives, or is not generally accessible or structured sufficiently for analysis and could include emails, contracts, documents, multimedia, system logs or other intellectual property. Parsing, tagging, linking or otherwise structuring or extracting usable data from these sources is considered the greatest immediate opportunity by most businesses among all types of data.

3. Commercial data: For many years, industry-specific data aggregators, such as e.g. D&B, Nielsen and IRI, have made available syndicated credit, real estate, postal, household and other data by subscription. Today, marketplaces are emerging for almost any variety of legally-available. Even privately among business partners, information assets are being used to barter. CIOs need to be aware of those that relate to their market and assess their potential as well as working with business partners to encourage the availability of their data.

4. Public data: Many governments have also begun opening their data coffers to support economic development, health, welfare and citizen services that are in various stages of implementation throughout the world. This data can also have significant mercantile value, especially when mashed with other data sources, to understand and act on local/global market conditions, population trends and weather, for example.

5. Social media data: Participation by individuals and businesses in blogging, tweeting, yammering, Facebook and LinkedIn updating has created another fast-growing, invaluable source of data about preferences, trends, attitudes, behavior, products and companies. Posts, trends and even usage patterns themselves are increasingly used to identify and forecast target customers and segments, market opportunities, competitive threats, business risks and even in selecting ideal employment candidates.

Build business leadership belief in data

IT must help business leaders understand the range of data available and business leaders must put this data into the context of organizational goals.

Many business leaders are still resistant to relying on data for decision making. Especially in matters of strategy, deep personal or professional experience, or multidimensional factors business leaders, rely on intuition more often than benefits their organization. In strategic decision making leaders tend to overemphasize past individual experiences, despite new or differing data indicating situational change.

Generate big ideas for big data

The biggest ideas for big data will likely come from outside your own industry. Adopt and adapt those that can help run, grow and transform your business.

The major opportunities for big data are not just around insular decision making or incremental improvements to existing business processes, they are around ways to transform the business and disrupt the industry by:

• Asking "chewy" questions that go beyond the mundane types of questions answered by basic BI tools such as, "How much did our business grow in the past year?" Instead they are questions that make full use of broader, deeper and more real-time data and, if answered and acted upon, could have profound effects. For example: "How can we increase customer shopping basket value by 20% and loyalty by 33% by better understanding their individual interests and behavior, and considering a range of economic forecasts and competitor moves."

• Radically changing business processes, as large quantities of data can give deeper insights than ever before. Operational processes can respond in real time or near real time to closed-loop stimuli; and the wider range of data available for integration and correlation can generate understandings of causality that enable predictive and prescriptive analytics.

• When introducing new products and services, product management, marketing, sales and information managers should work together to determine how big data can lead to the development of new offerings, e.g. identifying new markets, identifying new feature needs for targeted submarkets, identifying opportunities for completely new offerings, personalizing and mass customization as well as aggregating, packaging and selling information products.

Big data doesn't dramatically alter the economics of acquiring, administering and applying information assets, but it does amplify it. No longer can organizations ignore the need to balance these information supply chain costs with the tangible value derived from information.

As information becomes ever more recognized as a corporate asset, irrespective of its shocking oversight as a balance sheet asset by the accounting profession, CIOs and CFOs need to get in alignment with how information asset costs and benefits are measured.

Doing so will engender the pragmatism required to justify big data initiatives.

Ultimately, analytics projects are a means of delivering aggregated and summarized information related to a particular business problem. With big data initiatives a greater component of that expense involves the data itself, so accounting for it financially is imperative. (Source: Adobe Marketing Cloud / Gartner Report: "Big Data Strategy Components: Business Essentials")

By Anjum Siddiqi