implementing a Big Data strategy

Success through data: Implementing your own big data strategy

In the wake of big data, analysing and visualising data has become a decisive success factor for companies. It has now become part of the daily operations for many. With the help of intelligent software solutions, patterns can be recognised in a variety of data that humans alone would not notice. Don’t miss out on implementing a strategy now for the correct handling of data in your company also and  profit from the far-reaching findings. These have a positive effect on almost all sectors of business.

As the latest version of the annual study “Creating Value With Data” by KPMG shows, a majority of companies (40 percent) use data analytics above all to monitor business development. Thirty-seven percent use the analysis to better understand their customers, and 33 percent to optimise human resources.
However, companies that use data analytics for different purposes are rarely completely satisfied with the results. According to the market researchers, the reason for this rating is that many companies lack a data-usage strategy.
The pioneers are the big companies, of which half (49 percent) already have a big data strategy. For smaller companies of 100 to 499 employees, the proportion is only half that amount (26 percent).

The following tips are intended to offer you suggestions on how to create a well thought-out big data strategy for your own company. Data analytics, after all, only achieves success with the right planning and the necessary skills.

#1: Create an awareness of the value of data

The most important requirement for the use of data is to create awareness of its value among all those involved. Among other things, the analysis and usage of big data brings with it an optimisation of business processes, creates a clear basis for decision-making or even increases in profitability.

In eCommerce, this means in concrete terms that, for example, data-based customer analyses can improve customer loyalty. In addition, the product portfolio can be more easily adapted and optimised to the preferences and needs of customers. Offers can then be precisely tailored to specific customer groups and also presented at the optimal time. Together with an active price management, whereby prices are adjusted dynamically to the current market and competitive situation, a decisive competitive advantage can be gained. Also conceivable is a reduction in the number of returned goods. By linking different data, the causes of returns can be discovered and, where possible, also remedied.
Detailed supplier analyses will allow specific classifications, in regards, for example, of their reliability. Using intelligent sales planning, stocks and orders can also be optimised. The company will understand its customers and customer groups, as well as their habits, even better, and can then advertise in a customer-oriented fashion, also creating specific offers.

#2: Rethink more structurally

Big data is not a stand-alone technology that is centrally controlled and implemented. Furthermore, it transcends all sectors – from management to marketing and from controlling to IT. Many findings from the analysis of big data will remain untapped if they do not arrive at the right location within the company. Accordingly, many companies will then face the challenge of a structural rethinking. Successfully implementing a big data strategy does not, after all, mean creating a whole new department. Instead, all departments have to grow and tread the path together from raw data to value-generating insights.  The participating employees will have to get used to the new processes and the new culture in dealing with data. It is also important here for management to lastingly drive forward this development.

#3: Make plans over five steps

The starting point for data analysis is often very different for different companies. Often it is necessary to generate the data first of all. This first step is termed data acquisition. For this purpose, for example, machines may be equipped with sensors or chips, or customer data collected. In the second step, the data must be tested for its relevance, prepared for analysis and then standardised. The next step, the data analysis, involves the actual interpretation of the data. Important to the uncovering of trends or interrelations, known as data mining, is a rough idea of what is actually being sought after. Hire people, therefore, who know how to search and interpret large volumes of data correctly. Next comes the visualisation stage. This is an important step since it serves as the interface between IT and the further areas of responsibility. Only when the analyses are presented in a comprehensible way can the insights from the data become visible. Finally, the results of the data analysis should be distributed accordingly. And ideally not only within the company, but also through your external communications.

#4: Getting the ball rolling

Every beginning is difficult, but the future belongs to data-driven business models. Many companies have no concrete idea of how big data can be implemented and their entry succeed. Important, in any case, is to make that start!
In the development and implementation, it may be useful to orientate yourself on a procedural model. A Bitkom working group has developed the broadest approach to implementation so far, since it assumes new data and new processes that can eventually lead to a new business model. Based on the current data and systems landscape, an assessment is performed by this model to then eventually prepare the IT for big data. After implementing big data applications, it is important to integrate new data sources and to use new data. As part of the “Reporting and Predictive Analytics” step, the data can be refined by analysing and evaluating it, before finally using it in newly designed processes. In the course of using the new applications, a continuous optimisation of the information technology and the processes used takes place.

#5: Receive support for your implementation

The above recommendations provide you a good foundation for implementing a big data strategy. Now have the right partner at your side with an intelligent software solution. In eCommerce in particular, the details will decide upon success or failure. With our Business Intelligence Suite, blackbee, you can consistently optimise your product data, avoid duplicates and create a clean database using product matching. This in turn allows for an easy and automated price monitoring.

Would you like to know more about data optimisation or learn how blackbee can improve your data quality? Then contact us now!