What is the Big Data?

Big Data penetrated almost every aspect of every industry and business function and are now an important factor of production, alongside labor and capital. We estimate that, by 2009, nearly all sectors in the US economy had at least an average of 200 terabytes of stored data per company with more than 1,000 employees. That’s twice the size of Wal-Mart's data warehouse in 1999.

So what is the Big Data? Big Data is a collection of data sets so large and complex that it becomes awkward to work with. Difficulties include capture, storage, search, sharing and analysis.

Companies churn out a tremendous volume of transactional data, capturing trillions of bytes of information about their employees, customers, suppliers, and operations.

Social media sites, smartphones, and other consumer devices including PCs and laptops have allowed billions of individuals around the world to contribute to the amount of big data available.

And the growing volume of multimedia content has played a major role in the exponential growth in the amount of big data. Each second of high-definition video, for example, generates more than 2,000 times as many bytes as required to store a single page of text. In a digitized world, consumers going about their day—communicating, browsing, buying, sharing, searching—create their own enormous trails of data. Same applies for our employees.

Big Data and HR

HR is one of the last areas to get transactional data and to get good information about employees. Partly it’s that HR is historically not that computationally focused.

We have identified five broadly applicable ways to leverage big data that offer transformational potential to create value and have implications for how organizations will have to be designed, organized, and managed.

For example, in a world in which large-scale experimentation is possible, how will corporate marketing functions and activities have to evolve? How will business processes change, and how will companies value and leverage their assets (particularly data assets)?
  • Creating transparency - Simply making big data more easily accessible to relevant stakeholders in a timely manner can create tremendous value. In the public sector, for example, making relevant data more readily accessible across otherwise separated departments can sharply reduce search and processing time. In manufacturing, integrating data from R&D, engineering, and manufacturing units to enable concurrent engineering can significantly cut time to market and improve quality.
  • Enabling experimentation to discover needs, expose variability, and improve performance - As they create and store more transactional data in digital form, organizations can collect more accurate and detailed performance data (in real or near real time) on everything from product inventories to personnel sick days. IT enables organizations to instrument processes and then set up controlled experiments. Using data to analyze variability in performance—that which either occurs naturally or is generated by controlled experiments—and to understand its root causes can enable leaders to manage performance to higher levels.
  • Segmenting populations to customize actions - Big data allows organizations to create highly specific segmentations and to tailor products and services precisely to meet those needs. This approach is well known in marketing and risk management but can be revolutionary elsewhere—for example, in the public sector where an ethos of treating all citizens in the same way is commonplace. Even consumer goods and service companies that have used segmentation for many years are beginning to deploy ever more sophisticated big data techniques such as the real-time micro segmentation of customers to target promotions and advertising.
  • Supporting human decisions with automated algorithms - Sophisticated analytics can substantially improve decision making, minimize risks, and unearth valuable insights that would otherwise remain hidden. Such analytics have applications for organizations from flagging candidates for further evaluation to succession planning models. In some cases, decisions will not necessarily be automated but augmented by analyzing huge, entire datasets using big data techniques and technologies rather than just smaller samples that individuals with spreadsheets can handle and understand. Decision making may never be the same; some organizations are already making better decisions by analyzing entire datasets from customers, employees, or even sensors embedded in products.
  • Predictive analytics - Big data enables companies to create predict future performance based on current and past behavior.
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