Implementation of Big Data Analytics Project In 5 Essential Steps



Being able to predict market trends and customer preferences is the dream of all business leaders. Data analytics applications have what it takes to make this dream come true. But be careful: the right technology is only one side of the coin. Up to now, capital, labor, and raw materials were the classic production factors that have now received a new and serious competitor: data. A lot of data. According to various estimates, the global volume of data is currently doubling approximately every two years and is becoming more and more important.

 But where does all this data come from? The answer to this is very simple: From anywhere and everywhere includes, for example, social media channels, mobile phones (smartphones), search engines, financial transactions, RFID chips (radio-frequency identification), GPS data (global positioning system), and a wide variety of merchandise management systems and almost any other digital application or device. The subject of cloud computing also plays an important role in the collection of data.

The greatest challenge, however, lies in the art of generating structured data from unstructured data in order to use it economically or scientifically. Thanks to big data analytics, we now have the option of interpreting the data quickly and precisely in order to be able to make relevant business decisions on this basis. Companies that have developed this big data core competence for themselves enjoy a strategic advantage. Because they are able to predict information such as market trends and customer preferences. Systems that support them in this process must above all meet two requirements: The quality in relation to the processing of data must be right, and they must be able to prepare data in a meaningful way so that it is useful for decision-makers.

Implementation of Big Data Analytics Project In 5 Essential Steps

It is an illusion to believe that buying an expensive suite of software that does everything at your fingertips. Therefore, you should first all have the patience and time to structure and implement your big data project. Good planning is the most important success factor when implementing such a data analysis system. Before starting a project, it is advisable to take the following five measures.

  1. Identify the problem: Before you invest time and money, first find out what problems your company is facing and how they would have to be solved. Are you looking for predictive trends in your market, are you looking for new customers or sales markets, or do you want to optimize your supply chain? Without a precise and structured plan, it could end up taking more time and money than is actually necessary and, in the worst case, will not lead to the hoped-for result.
  2. Define success factors: Take sufficient time in advance to identify those key figures that are required for the progress of the project and the expected results.
  3. Identify key data: Before pursuing a particular software or solution, make sure that you have full access to the relevant data and that you have a sufficient amount of data (information). No project without data.
  4. Assemble talent: do you have the right employees? A suitable team has the appropriate know-how to be completing the project before, during, and after implementation to be able to ride. If this is not the case, your project is doomed from the start. Make sure in good time that you have the right people in-house or get external support.
  5. Testing, testing, testing: Before using your big data development in live operation, make sure that you have tested your system sufficiently and the information generated correctly. After all, you want to give the decision-makers the right information and not lead your company down the wrong path. Finally, an important note: Remember that a big data system is constantly evolving and such a project will never be completed. New problems arise, the business strategies of the decision-makers change, new markets are added or customer behavior changes over the years. Regular adjustments and controls as well as the further development of the big data project are essential even after the system has been commissioned. Comprehensive tests include the following test criteria:
  6. Functional tests or functional tests
  7. Non-functional tests
  8. Interface tests
  9. Failure tests
  10. Data consistency tests
  11. Restart tests
  12. Interoperability tests
  13. Installation tests
  14. Surface tests
  15. Stress tests
  16. Crash tests
  17. Load tests
  18. Performance tests
  19. Computer network tests
  20. Security tests

The test itself can be carried out according to various methodological approaches and is based on the test strategy (SMART testing, risk-based testing, data-driven testing, exploratory testing, top-down/bottom-up, hardest first, big-bang).

Implementation and Operation

The first step in the practical implementation of the big data strategy is the implementation of the selected platform according to the plan from the architecture phase. We take on the installation and configuration of the system either independently or, better, in cooperation with the customer’s team, which then takes responsibility. In addition to the installation of the big data platform itself, this also includes the connection of the various data sources and the normalization of the data for successful evaluation and correlation. In some cases, we also develop new approaches for integrating unusual data sources, or we implement additional software components for any data pre-processing that may be required.

In this phase, we also develop the required roles and rights concept with the customer. It is often difficult to decide in advance “at the green table” which roles are required, which user groups should access which data, and who should perform which functions. During the implementation and connection of the data, it often becomes clear very quickly how granular the access controls should be set up in order to meet all requirements. For us, a central component of this phase: the documentation of the customer-specific solution and the data sources that have been set up. Even a small big data environment quickly turns into a complex structure, precisely because a wide variety of data from distributed systems often converge in it. Consistently maintained documentation (e.g. in a wiki) makes later work with the new system considerably easier.

Even after the first phase of implementation, the big data solution needs ongoing care and maintenance. Often new data sources are added, the existing data sources change or there are interruptions in the delivery of data due to problems in the IT infrastructure. In order to ensure the desired availability of the data in these cases as well, the operation of the big data platform must be planned and implemented just as professionally as for any other IT system. On request, we also support our customers during operation and take over all necessary tasks, including the necessary updates and extensions.

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