Nowadays, we are living in a world with a specialization in data and issues related to it. Managers are sometimes overloaded with data through reports, dashboards and systems. We need to use data for making decisions. Senior leaders need to capture big data in order to develop initiatives.
The role of data is always on demand. Companies are spending a lot of money annually in order to install software for capturing, storing and making analysis for data. marketing departments are highly filled with technical and data specialists when it comes to creative roles.
The world of business is focused on data, but it is more vital to realize. That data is not an end automatically. Similar to every other thing that we are working on, data is a tool coming with advantages. If it is approached properly, it will bring about many potential to make decisions effectively.
On the other hand, it comes with a lot of risks and challenges. The following post will provide you with a clearer picture when it comes to the possible pitfalls that may arise.
Poor data quality
In spite of the fact that we get familiar to considering the quality regarding physical objects or products, it is recognized that data quality is a material issue for every company. Data which is placed in structured databases is often incomplete or even outdated. It means that you may get an end of a simple example of a data quality problem.
Many people can recall getting copied mailings from marketing campaigns delivered with a little difference in their names. The marketer’s databases come with duplicate records with customers’ address and variations of their names. Repeating such mistakes by millions of records will make the companies end up costly spending.
The problem of data quality is becoming more and more important because people try to make decisions regarding strategies, markets as well as marketing campaigns in real time. Despite the fact that software and solutions are taken to deal with the quality of structured data, the real solution is a significant commitment to work with data as a valuable asset. In fact, this is really challenging to succeed and often asks for more disciplines and support from leaders.
Data is located everywhere in a company. For instance, most organizations have different departments that need to capture information regarding their clients and prospects.
More precisely, marketing gathers data from people joining live or web events or downloading something from the websites. Executives take advantage of data in order to generate new ideas or strategies. On the other hand, sales capture data about clients who took part in the sales process whereas customer service department gathers information after calls and online chats. Management teams collects data and main figures for scoring cards and so many other teams that need this data.
Customer information is collected in a lot of different software systems then it will be stored in different data repositories in the company. Data is now located everywhere and it is often the case that data is available from both social and search feeds in real time. If the data can not be accessed quickly and conveniently, if we have duplicates or incomplete data types, it is almost impractical to make the most of it for our goals and objectives.
Also, many more companies are integrating their disparate software apps and making the process of gathering and aggregating data throughout their organizations in a simpler manner. Nevertheless, when it comes to data quality, this effort is rather costly, time-consuming and endless.
Growing data values
Today, people are generating more and more data at a really fast pace which is sometimes complicated to understand. According to experts, people are creating more data then existed on the Earth in the other year.
Most of the new data is unstructured in comparison with the data that comes to our software and database apps. For instance, all of the tweets regarding your products or services are a potential value of insights. However, this data is unstructured, thus it will increase the sophistication for people to capture and make analysis. Although there have been many software and solutions coming to deal with this challenge, the unstructured data acts as a new torrent of raw material to process, with all complexity and quality issues that we are taking a look in this post.
Garbage-In and Garbage – Out
Data analytic software is only as effective as the data coming into it. The common point when it comes to using data for benefits is its quality. Although many companies make big investment into powerful new apps to crunch data, the task is no use if the data is dirty. There is always the need to trust the data sent to crunch.
Data analyses are not conclusive.
It is often thought that the outcome of data analyses is conclusive. However, that may be not the case. In fact, data analysis usually reveals correlation instead of causation. It is easy to run into the trap of supposing that the output of data analyses is conclusive and not being able to differentiate correlation with causation.
Correlation means a relationship while setting up a causal relationship is important to make accurate decisions, which is so difficult to achieve.
Regarding assessing data, the cognitive biases of people may be amplified. When you come to the point at which you need to make an evaluation for the meaning of the data analysis, this is when your biases will come. You may consider that you would trust or depend on data supporting your positions and expectations while ignoring the data that does the opposite. You also trust data from the sources you get familiar or you prefer, otherwise you rely on the data that is the most lately. However, all of the biases make a contribution to the difficulties and errors when you analyze your data.