BUSINESS RESEARCH

Big Data Management

The era of ‘Big Data’ is rapidly evolving in ways that are substantially different to ‘analytics’ of the past. As of 2012, about 2.5 exabytes of data are created each day, and that number is doubling every 40 months. Walmart, for example, collects more than 2.5 petabytes of data every hour from its customer transactions (a petabyte is one quadrillion bytes, or about 20 million filing cabinets’ worth of text).

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October 2012

Volume is not the only issue – the speed of data creation is even more critical. For example, a study by the MIT Media Lab used location data from mobile phones to infer how many people were in Macy’s car parks on Black Friday – the start of the US Christmas shopping season. This allowed researchers to estimate the retailer’s sales on that critical day – even before Macy’s itself had recorded its sales.

Big data is fast becoming a new corporate asset, but to leverage a competitive advantage, companies must address five fundamental management challenges:

Leadership: Companies succeed in Big Data because they have leadership teams that set clear goals, define what success looks like, and ask the right questions – not just because they have more or better data.

Talent Management: Great data scientists are hard to find – companies must attract individuals with skills in cleaning and organising large data sets and who have expertise in designing experiments to help cross the gap between correlation and causation. They must speak the language of business to help leaders reformulate challenges in ways that Big Data can tackle.

Technology: The necessary technologies are not prohibitively expensive – in fact, many are open source. However, such technologies require skill sets that are new to most IT departments. IT will need to work hard to integrate all relevant internal and external data sources.

Decision Making: In the Big Data era, knowledge workers create and transfer information, and expertise is not always where it used to be. Effective companies need to bring together the right people with the right data, as well as the people who have problem-solving capabilities that can effectively exploit them.

Company Culture:  Data-driven companies do not ask themselves “What do we think?” but rather they ask: “What do we know?” They don’t pretend to be more data-driven than they actually are, and they don’t spice-up reports with lots of data to justify decisions that have already been made using traditional approaches.

Referenced techniques

Technique

Consumer Behaviour

Understanding consumer behaviour has significant bearing on marketing and public relations decisions. The concept reviews a number of issues related to consumer behaviour and organisational responses and will help you to enhance your marketing campaigns to successfully connect with consumers.

Technique

Data Warehousing and Data Mining in Business

Data warehousing and data mining have emerged as key technologies and essential components of modern decision support systems. Strengths and weaknesses and success factors are considered and practical steps are provided to help organisations implement successfully.

Technique

Information Management

The concept is created to help practitioners better understand the notion of information management, its history, practical use, implementation strategy and limitations.

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