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9 Key Considerations for a Successful Big Data Strategy

Big Data strategies help companies gain insights and optimize processes. It creates new business models, markets and new growth. But where do you begin? The following 9 considerations may help you build a successful Big Data strategy for your company.

1.People with different skills

The exploration and exploitation of Big Data is relatively new as a phenomenon. The requirements are more extensive and complex, and Big Data task is not just part of the IT departments; it belongs to the management of a company.

If computer science, statistics and mathematics are among the competencies of a “Data Scientists”, but Big Data requires far more knowledge. If considering Big Data as a whole process, it cannot be fully handled by Data Scientists. Instead, a team of experts with various skills are required. For example, the team should have knowledge in the areas of IT, statistics, mathematics, graphic design, psychology, marketing and knowledge of the relevant fields.

2.New business culture

Companies face the challenge of structural rethinking. The findings from Big Data will affect all departments in a company. Successfully implementing a Big Data strategy is not to create a new department, it is to create a new culture.  Depending on the project task, the type of data, by company size and available resources, the demands on Big Data strategies will look different.

3.Legal issues

In all data collection activities, companies need to ensure that they are consistent with the prevailing laws. Privacy and anonymity should have a high priority.

There is no border for the data your collected. However, privacy laws and regulations will be different in different regions and countries. For example, when analyzing data from social media, forums and blogs, it might not under the consideration of the data protection law in a particular region or country. However, there is still legal clarification required for data storage and re-use of historical data.

4.IT considerations

Data storage, accessibility and security are the three core tasks IT departments need to consider. Accordingly, enterprise data architecture should meet all these three requirements. For example, storing data in the Cloud is more scalable; but to store sensitive data in a Cloud has a higher risk than on an internal server. When considering a Cloud solution, additional encryption strategies may require.

5.Analytical methods

In traditional databases with so-called structured data and data warehouses, the classical methods of computer science are in demand; for example, using the pattern generated from mathematical and statistical methods to indicate anomalies and to identify correlations.

Similar tasks such as predictive analytics, data mining, data forecasting and fraud detection are also among the most important applications for Big Data Analytics. In addition, Big Data also enables machine learning, or “Deep Structured Learning” – machines can recognize optimization potentials capable and implement after working a few mathematical algorithms, rules and laws against Big Data.

6.Data diversity

Data from different sources are not the same. They can be structured, unstructured or a mixed form. Depending on what type of data a company has to work with, other skills may be required. For example, images and PDFs require a different analysis technique as metadata or data streams from sensors that monitor the operation of machinery.

The analysis of data from social media cannot be carried out statistically. The meaning of words and context are crucial for the evaluation and utilization of text data. Expertise from the linguistics and text analytics is therefore necessary.

7.Three important trends

If a company operates a comprehensive Big Data strategy and works with different data types, traditional database technologies are insufficient. To survive in the market, new technologies for Big Data are required:

  • NoSQL: The “No” stands for “Not only” SQL. This is a type of database that can “not only” deal with structured data, such as a pure SQL database. This is important because certain relationships are only produced through the combination of different data types.
  • Hadoop is an open-source solution that makes it possible to store huge amounts of data inexpensively.
  • In-memory computing: primarily relies on keeping data in a server’s RAM as a means of processing at faster speeds. In-memory computing especially applies to processing problems that require extensive access to data –analytics, reporting or data warehousing, and big data applications (IBM).

8.Data Visualization?

To take advantage of Big Data, the insights from it needs to be generated in the right format, arrive at the right place, and deliver to the right person. To achieve this, data analysts and data artists in the company will need to translate the results of the analysis into charts, graphs, images and text.

The visualization and presentation of the results go hand in hand with the interpretation of data. Big Data becomes a powerful only when the insights and context are clearly presented.

9.More than just data

Big Data is not a single technology. The economic benefit of Big Data results from the integration of the legal framework, technology, data security, finance, communication and operation.

The interaction of many technologies and professionals can make Big Data a valuable tool. It can improve products and processes, new business opportunities, analyze the competition, financial plan, optimize prices, find new distribution channels, identify trends, and provide security.

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