The Biggest Challenges Around Big Data

Big data is one of the buzzwords of the hour. The amount of data that many companies have is .Anyone who comes into contact with “Big Data” in a business environment is sometimes confronted with it in the form of a marketing term. The meanings change depending on the context in which people use the term. This is also because the term is still quite young, and there is no uniform definition.

However, this always refers to the amount of data that we all produce and have produced through our online activities, which can hardly be evaluated using manual and conventional data processing methods. They are too big, too complex, too fast-moving, or too weakly structured. But with a look at the trends in big data in 2020, it is also clear that the technology can enable exciting new possibilities, such as the “Shazam” of data. But there are still major challenges that many companies face when it comes to big data. It starts with the fact that many companies don’t even really understand what big data is and its relevance.

Approach Big Data And Understand The Topic

One of the biggest problems with big data is that many companies still have no idea how relevant the topic is. Managers often do not even know exactly what they are dealing with when it comes to big data, let alone the advantages of working with it. Questions of the infrastructure for data analysis, suitable responsible persons and experts in the company, and further steps are therefore not even dealt with.

The first challenge is understanding big data and accepting the need to implement it in your own company. This step must be started by top management. Employees can be motivated and inspired to adapt existing processes and use big data to grow all business areas. As a rule, however, this also requires training courses and workshops carried out regularly by a qualified IT department.

Understand The Benefits Of Big Data

The basis of using Big Data for a company is understanding the advantages that can result from evaluating the amount of data. The following three advantages, for example, can be understood immediately and are of great benefit for every company:

  • Big data helps to extract hidden patterns and information. The knowledge enables business decisions to be made more confidently and confidently. Real-time analyzes even enable faster decision-making than was previously possible.
  • To remain competitive, companies today have to develop strategies to save costs while still delivering better performance. What sounds like an impossibility can work with the help of big data. Huge amounts of data can arise for all areas of the company. With high-performance analysis systems, this information can be processed and combined to make numerous processes more efficient.

In the areas of development and research, customer data are the be-all and end-all. They make up a huge part of big data. If they are professionally analyzed, trends can be read off or even predicted at an early stage. This, in turn, helps with product development or with marketing strategies and improvements to existing services.

Practical Examples

A simple and concrete example of increasing efficiency through big data is the transport of goods. Different factors influence the transport of goods by truck. This can be congestion data, weather data, or current gasoline prices. Most of this data is measurable. With the help of professional analysis systems, the trucks’ routes can be optimized so that as much time and costs as possible are saved during transport.

The energy industry also benefits enormously from data these days. Things like smart homes or smart grids ( smart grids) have a formative influence on future energy systems. The communicative networking and control of power generators, storage systems, and electrical consumers generate huge data. With the help of certain software platforms based on artificial intelligence, this data can be analyzed, prepared, and processed in real-time. In this form, they provide important information about, among other things, optimal power distribution and power generation.

Calculate The Financial Outlay For Big Data

The implementation of projects around big data is also associated with costs. These costs can be quite high – especially if big data has not been an issue in the company. It is therefore important not to underestimate the costs, but at the same time also think about how great the opportunities that arise can be in the long term. It then has to be weighed up how much money can flow into big data projects. Decisive cost items can be the following:

  • New employees who specialize in Big Data have had to find the company gradually. Developers, administrators, specialized data scientists, and big data analysts want to be well paid. They are currently more in demand than ever.
  • New hardware and software are also necessary to implement big data in the company.
  • Additional financial resources are required to develop, install, install, and maintain new software or individually useful software components.
  • Electricity costs will undoubtedly rise as well.

To meet the financial challenge of big data, it is necessary to carefully analyze the specific technological needs of the company and the goals. If flexibility is then required, for example, cloud services can be the optimal solution. For companies with extremely high-security requirements, on-premises solutions are likely to be more suitable.

Dealing With Data Growth Properly

Logically, one of the characteristics of big data is its constant and unstoppable growth. It is estimated that the global amount of data in IT systems doubles every two years. Companies are responsible for much of this information. Since most of the data is unstructured, that is, it is not stored in any database, the management of the data is becoming more and more of a challenge for companies.

Without a good architecture of the company’s big data solutions, efficient use of all this data is no longer possible. They reduce the risk of a performance drop in the system or a sudden blow to the budget. In addition, the maintenance and support of the system in the company should be well planned. This is the only way to account for any changes related to data growth properly. Systematic profitability audits are also an option. Because they can help identify weak points and correct them in good time.

Use Results From Big Data Projects

As promising as big data projects are approached in companies – they often end in disillusionment. Because they always start as exciting experiments but do not deliver the expected added value. This is also because the scope of the projects is often still underestimated. It has long been known that the cost of selling a product or service is only a fraction of the cost of customer acquisition.

Analyzing big data always also means understanding the customers. Enriching customer data with big data results and thus creating a simple and complete view of customers can be costly. Optimized customer analysis in real-time, however, improves the customer experience so drastically that in the end, these costs can usually be reimbursed umpteen times.

Another problem is that big data analytics often check which products are bought, how, and when, based solely on their historical data on customer behavior. However, several factors influence consumer behavior online in the digital age. For example, a single post from an influencer on social media can cause such a trend that the data available so far cannot provide any information about it.

The company’s existing big data tool may not analyze data from competitor social networks or online shops. In the worst case, these, in turn, use big data to identify such trends in social media – and that in real-time too. Potential customers then turn to the competitor who is better informed through data and acts accordingly.

Therefore, the challenge is to create a correct system of factors and data sources in which the necessary insights can be obtained through the analysis. This can ensure that no factors are lost sight of. Such a system should also contain external sources – even if it can be difficult to collect and analyze external data.

Also Read: Big Data: Definition And Examples

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