Common Pain Points In Today’s Big Data Projects

The advantages and competitive edge that Big Data can provide to a company are really tremendous. According to Gartner, 89% of business leaders today expect to compete based on customer experience only. Big data helps businesses to dive deeper into consumer behavior and to develop an understanding of the customer. As such, the majority of companies is taking on Big Data projects or is planning to do so in the coming future. But a vast majority of these projects faces obstacles in their implementation. There are many pain points associated with any Big Data project today. Let’s take a closer look at some of them.

Unconsolidated Data

The first and probably the most important challenge faced by Big Data projects is unconsolidated data. Data may be trapped in many different places and consolidating data through such media becomes difficult. A typical set of such data includes structured data from in-house systems and unstructured data from sources such as emails, logs, social media, etc. which may be distributed across multiple databases. Often, data required may not even be digitized as in customer feedback forms of a big retailer. This unorganized data presents a very big challenge for experts as any big data project is as efficient and productive as the data analyzed. If any project misses out on important sources of data due to such issues, it hampers the project’s objective.

Data Volume and Quality

The rapid pace of digitization has aided companies in collecting huge amounts of data from many sources like social media, web-based tools like cookies, surveys, consumer feedbacks, advanced sensors deployed recently like beacons, etc. This has created another challenge for companies – which data to retain and which data to discard? As the volume of data collected increases, data warehouses and analytics engines are unable to cope up with the huge demand. Most of the raw data contains useless and redundant information. As such, companies must use proper tools and consult with expert Big Data developers to consolidate and classify the raw data, which can then be used in data visualization. This helps in creating a more understandable format and can be easily used by analysts in their further processing.

Fault Management Strategy

Although business leaders are well aware of the progress that Big Data projects can help them to attain, their outlook towards the same has not changed much. In recent studies by Fortune Knowledge Group, 61% of respondents claim to trust their guts and 61% believe that real-world experience beats data analytics in making decisions. Most companies still interpret Big Data as an IT investment rather than a marketing initiative, and as such are reluctant to assign a large part of their workforce exclusively to data mining. They often outsource such activities to third-party vendors. But what they fail to realize is that Data Mining is not simply a mix of statistics and programming. Domain knowledge is the most important aspect of any Big Data project, without which 3rd party programmers tend to deviate away from the main focus of the project, which they obviously do not understand. Companies should initiate efforts to develop data experts in-house or should make sure that the 3rd party provider understands the goals perfectly and align their actions accordingly, which can be best achieved via proper and open communication between both parties.

Insufficient Infrastructure and Lack of Analytics Skills

Many companies are facing obstacles in their data mining projects due to the lack of proper infrastructure. Data is often distributed amongst many clusters and departments. The major challenge, as we have mentioned above, is to organize such data into meaningful structures, the infrastructure for which is not readily available. Although organizations like MapR and HortonWorks are updating their cloud-based Hadoop platforms to deal with jumbled data, this leads to more enhancements due to security concerns.

Also, skilled workforce is tough to find. Data scientists with a proper blend of knowledge of programming, particular business, statistics and analytical tools are required to successfully complete Big Data project, but presently there is a dearth of such workforce and people with the right skills are sought after aggressively by organizations. As an alternative, companies have been using free online training materials from SAP or other brands to make their in-house analytics staff acquire new marketable skills.

Sources: Gartner, 2016; InformationWeek, 2016