Top 5 Data Analytics Challenges
Data analytics is very important for decision makers. This helps improve decision making, increase accountability, promote financial condition, and monitor organisation performance. However, achieving these benefits is not as easy as it sounds. There are several challenges to collect and use the business data. Fortunately, there is a solution to overcome these challenges.
In today’s modern world, organizations uses statistics and enterprise analytics to enhance decision-making, monitoring business growth, boosting productivity, and achieving the competitive edge. However, many companies have issues with the usage of enterprise intelligence analytics on a strategic level.
According to Gartner, 87% of corporations have low BI (enterprise intelligence) and analytics maturity, missing statistics steering and support. The issues with enterprise BI aren’t simply associated with analytics however it can be because of non-availability of infrastructure.
Here are the top 5 data analytics challenges that businesses face:
1. Lack of skilled resources with understanding of Big Data Analytics
The evaluation of different data (variety) is crucial when large quantity (Volume) of data is being produced each minute (velocity). The massive flow of data generated is creating exponential opportunities for Data Scientists and Big Data analysts in market. It is crucial for organizations to hire a Data Scientist in limited budget having multi-disciplinary competencies who understands Big Data evaluation and have capabilities to work on Velocity, Variety and volume of data, Technologies, and the Business Operations as well.
2. Gaining meaningful insights using Big Data Analytics
Using data is only as good as the questions that you are seeking to answer. Competencies are the biggest barriers when it comes to generating meaningful insights using the big data. Lack of structured data engineering methodologies is the most technical barrier in deriving insights.
3. Bringing extensive data to big data platform
It is usual that data is getting generated with each passing day. This suggests, organizations need to address a massive quantity of data on an everyday basis. Loading and Transforming the data into the data warehouse becomes challenging at times due to multiple data sources where the data engineering skills becomes important to make data accessibility smooth and handy for Analysts and Reporting Managers.
4. Uncertainty of Data Management Landscape
There are many competing technologies available within each technical area e.g. ETL tools, Visualisation tools, database technologies like OLTP/OLAP etc. There are numerous options available to choose from. The challenge is making the best choices while not introducing additional unknowns and risk to big data adoption.
5. Data Storage and fast retrieval
With the widespread business operations and verticals, businesses are generating the large amount of data which will be increasing further day by day. The data storage and accessibility of data becomes important and creates a need to have data lakes/ data warehouses which can store, process, and retrieve the data whenever required. The actual hassle arises while data lake/ warehouse tries to integrate unstructured and inconsistent data from various sources, and it encounters errors. Check our guide to handle incremental data processing.
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