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Synopsis

People can only be classified by the data they generate in a limited number of ways. It is upto the data analyst to gather as much data as possible, and by examining vast amount of information in the form of data available from diverse sources, uncover hidden patterns, unknown correlations and other useful

information that can be used to make a better decision.

This unceasing data has changed forever how the business operates. Many businesses are looking to it to tell them more about their customers, their buying habits, and the likelihood they’ll behave in certain ways under certain

circumstances.

Most of the senior executives, consider leveraging big data to be a top strategic priority, as they seek to gain customer insights, effect more accurate budgeting and better performance management, and develop new products.

Huge gap exists between what organization hope to accomplish and what they’re able to, given their existing IT infrastructure & expertise. This opens doors to all kind of new risks.

The big data is not to be considered as a single set of data, but it is the way data grow and when there is some kind realization in the ways that might be able to connect different sets of data together to create even more sets of information.

In a nut shell, Big data represents the ever – expanding collection of data sets that sheer size, variety and speed of generation make difficult to manage and harness information from.

The source of these electronics data are varied, being as simple as transactions in a banking system, or as complex as unstructured content such as Facebook pictures and videos.

One thing that experts world – wide agree upon is that big data is not simply data mining on a greater scale, it can be thought of more as a survey or

prospecting. On the other hand data mining is conducted on structured data from a limited set of sources.

Questions Boards Should Ask

■Does the company have the talent and technological capabilities needed to collect, manage, analyze, and store big data securely and effectively?

■Does the company have an established policy that addresses the ethical considerations of collecting, analysing, and using sensitive information? Is the policy well known?

■Does collecting and using big data subject the company to any additional compliance risks?

■What is the company’s strategy for using the data it collects?

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■Is the chief information officer able to explain effectively — in layman’s terms — what big data is, how it is stored, what the risks are, and how the risks are

managed?

Why invest in big data business?

Big data business is a very diverse field, representing an interdependent

ecosystem. In layman language, it can be divided into three specific categories:

Data Users: Use data for internal purpose

Data Suppliers: Supply data as a product for others to use

Data Facilitator: Are organizations that help others to exploit data

 This includes businesses that provide outsourced analytics services and those offering either infrastructure or consultancy on data strategy.  This function is especially important during the transition phase because

many potential users of big data do not have the skills to implement a big data strategy internally.

Many business models focus on the use of data within and between businesses. However, as consumers are themselves increasingly faced with an abundance of data there is a growing market for businesses that provide data-driven products and services to end users.

 The growing market for personal health and fitness devices, along with smart home technologies are pro-typical examples.

 The whole spectrum of business models also apply in the consumer-facing segment: consumers will increasingly demand data analysis tools and services, data focused products, and help and advice for managing data their challenges.

Various practical and political hassles of the business?

The most commonly cited obstacles can be essentially divided into two categories: practical obstacles concerning data availability and quality, along with the necessary resources for analysis; and political obstacles that shape the environment in which data is used.

 An important practical obstacle is the quality of data sets. Some experts say analysts spend as much as 90% of their time cleaning data. Data, especially government data, is often provided in non-machine readable or non-standardised formats requiring manual re-entry.

 Past experience highlights the importance of being forward-looking in anticipating future uses (and users) of data. Legacy datasets that were not stored with appropriate human-readable metadata are now essentially useless because nobody knows what the data mean. A similar point holds for the format and physical infrastructure in which data is stored.

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non-specialists are an exciting prospect, but are still not mature enough to solve the problem.

 A key political barrier to data use is the extent to which people are protective of ‘their’ data. This often applies to a reluctance to share data within an organisation as much as to an unwillingness to share data between organisations, and speaks to the need for an organisation-wide policy and strategy for data use.

 There is widespread appreciation of the importance of privacy, but

managers bemoan the lack of standards and clear policy guidance in this area.

For achieving success using big data, a well-defined business model needs to be prepared centred on big data?

Data should be central to the business. The biggest success stories have either essentially reinvented their entire business around the use of data or are ‘born’ data users.

A clear profit model is essential. Experts warn that optimistically collecting data in the hope that it will somehow prove profitable is naïve. Managers and data scientists should be clear on the plan for generating value or efficiency from data before the data strategy is implemented.

The most successful firms understand the limitations of the technology behind their big data operation and recognise the importance of combining analysis with a sound understanding of the context, a good intuition for the industry, and a critical attitude towards insights derived from data.

Having the right skills is crucial. Very few individual possess the right

combination of statistics, coding, and business skills, so cultivating talent in teams is essential.

Global Big Data Survey Trends:

In the past, databases tended to be limited—they only had to meet the demands of human users entering and retrieving data. With the emergence of ecommerce and internet search engines, database technology is evolving to manage humans and computers. Today, with the amount of data growing by 50 percent each year, it is information technology that is capable of managing, processing, and finding value.

Key Survey Highlights:

 Data overload and the abundance of trivial information are challenges many organizations face. There are a lot of data in general but also a lack useful data. High-level data are available, but not the detailed data

essential too many decisions, plans, tasks, and functions.

 Important data are not reaching practitioners in efficient timeframes. Fifty percent of respondents report there is an undesirable delay in receiving information about actual sales, demand forecasts, customer changes and orders, and materials or component shipment status.

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paper records are used in many cases. Separate databases serving

departments or partners in other nations may be used. Access to this data may depend on factors practitioners are not aware of, such as availability or security.

 Current information technology has not yet delivered optimal satisfaction in terms of what is easily measurable, reportable, or quantifiable data such as scheduling, inventory levels, and customer demand across the supply chain. Survey results suggest possible reasons, such as different data formats and systems, and timeliness and data access challenges. This may challenge processes such as sales and operations planning (S&OP) that seek a shared and integrated understanding of supply and demand.  Supply chain dataflow includes direct suppliers and customers, but there

are gaps in a complete, or true end-to-end supply chain dataflow model for most respondents. For example, only about half of survey respondents report that logistics, distributors, and minor direct suppliers are part of their supply chain dataflow. True end-to-end supply chain dataflow, including suppliers’ suppliers and customers’ customers, remains a challenge. Supply chain and operations management professionals have more work to do in terms of improving tools, technologies, strategies, and relationships, and big data can play a major role in this progress.

Big data is an up-and-coming skill and knowledge area. It may pay to make big data a professional development priority because the expertise will be rare and in demand. In addition, big data will probably be implemented in long-ranging projects in increments as technology and resources permit. If big data were to resemble past ERP implementations, practitioners can help develop project management skills and high-level implementation ideas now and share those with management teams. This interesting and thoughtful approach may position a professional to advance in his or her career, improve a team’s performance, or gain strategic knowledge of an organization’s supply chain and operations

management environment.

Big data best practices:

Developing good relationships with supply chain partners. Good relations are necessary to facilitate shared data and insight. Building a foundation with these relations in place, before one begins asking partners for advanced data sharing.

Addressing supply chain dataflow gaps where possible as soon as possible. Organization may have developed procedures or practices that make such gaps manageable, but are not as good as they could be. As powerful as the promise of big data is, it probably can’t correct processes and procedures built around current gaps.

Looking for areas of correlation that don’t seem obvious now, or seem too complex to study at this time. For example, if demand decreases at times when it is expected to increase and there isn’t a clear explanation, this is a good assignment for big data systems. A new big data implementation may not

otherwise know what has high priority to the organization in terms of correlation and investigation.

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