Due to the time it might take, businesses may end up making errors that can negatively impact activities. As such, businesses must learn from these mistakes and improve their big data strategy going forward. To begin with, you need to understand some common problems that can significantly affect your big data project and its implementation. Knowing these problems may help you to prepare adequately and prevent them.
- Emphasizing on technology instead of the business
Often, IT leaders focus on the infrastructure required for big data solutions as opposed to the business requirements of the intended solution. They may concentrate on the storage and the compute space required and end up making decisions based on these aspects. Their focus is may be controlled by the rising costs of the big data infrastructure. Rather, business leaders need to focus on the outcomes of big data initiatives on business. This will ensure that there is a business context in a company’s big data initiatives so that IT can deliver what business requires rather than a business trying fitting into what IT demands.
- Too much data
In the current age of information, data is streaming in from every direction. With a large number of websites, applications, mobile phones, and other smart devices, sources of data are many. Consequently, collecting and mining data from these sources is extremely difficult and may be disastrous if not planned appropriately. However, many organizations fail to utilize big data and investment in this area end up being wasted. To ensure that your organization manages data that streams in well and avoid confusion, determine which data is needed and what is not required.
Large amounts of data lead to complexities and many other problems. Collecting such huge amounts of data can result in difficulty and as a result makes it hard to extract insights the business can use. Data scientists should have a structured plan that highlights the business objectives and poses questions that data should be able to answer. This kind of strategy makes it easier for discoveries to be made.
- Poor data quality
Poor data quality costs companies huge amounts of cash according to Gartner. Accordingly, companies lose approximately $15 million yearly as a result of messy, unstructured data collection practices. The losses can increase with the complexity of data sources and huge amounts of data collected from different places. Poor quality data may have adverse effects including information crisis, time waste, and waste of resources used in organizing the data. Furthermore, poor quality may mean that it will need to be entered manually. Manual entry of data may also introduce typographical errors and other human-related issues. This might lead to grave mistakes which impact decision making adversely.
In general, any company that intends to venture into big data must have realistic expectations and try their best to align their big data strategy to business needs. It will not only ensure that the company meets its goals but will also ensure the attainment of profits.