Integrating data into strategy is proving to be a key differentiator for businesses of all sizes. The commonly used term “Data-Driven” is not just for the unicorns; even companies like DiscoverOrg and MVF are using data to help drive decisions and create better products. SMBs are finding savings and revenue opportunities, thanks to “Data.”
So, just pulling data from everywhere is not always going to be enough. There are a lot of problems – which can come up with developing your data strategy and products. Let’s look at some of the difficulties as an enterprise owner: you are likely to use data, including data size, consistent data, and definitions. You are also reducing the time to get data from third-party systems to data warehouses.
Challenge 1: Lack of Proper Understanding of Big Data
Companies generally fail in their Big Data initiatives due to insufficient understanding. Employees may not know what data is – its storage, processing, importance, and sources. Data professionals might understand, but others might not have a clear view.
For instance, if employees do not know the importance of data storage, they might not keep the backup of sensitive information. Also, they might not use the database properly for storage. And the result would be – when you require the vital data – you won’t be able to retrieve it quickly.
Solution:
Extensive data workshops and seminars should be held at the organisations for everyone. Basic training programs must be arranged for all the employees handling the data regularly and participating in the Big Data projects. All levels of the organisation should include a basic understanding of data concepts.
Challenge 2: Data Growth Issues
One of the most critical challenges of Big Data is storing enormous sets of data properly. The amount of data being stored in data centres and databases of companies is increasing rapidly. As it grows exponentially with time – it gets tough to handle. Most of the data is unstructured and comes from documents, videos, audio, text files and other sources, which means you cannot find them in the database.
Solution:
Companies opt for modern techniques to handle large data sets – like compression, tiering, and deduplication. Compression is generally used for reducing the number of bits in the data, thus reducing its overall size. Deduplication means deducting duplicate and unwanted data from a dataset.
Data tiering lets you store the data in different storage tiers, ensuring that it resides in the most appropriate storage space. It can be public cloud, private cloud and flash storage – depending on the data size and importance. Companies are opting for Big Data tools like Hadoop, NoSQL and other technologies.
Challenge 3: Confusion with Big Data tool selection
Usually, companies get confused while selecting the best tool for Big Data analysis and storage. Like is Hadoop good enough, or will Spark be a better option for data analytics and storage? Which one is a better technology? HBase or Cassandra? All these questions generally occur when the management goes for purchase. And at times, they end up making poor decisions while having insufficient knowledge about the same. And at the end of the day – money, efforts and work hours are wasted.
Solution:
One of the best ways to solve such problems is by seeking professional help. You can either hire experienced professionals or go for Big Data consulting. The in-house experts will help you with the tools and techniques, and consultants will recommend you the best tools best in your organisation’s current scenario. And based on this, you can work out a strategy and select the best solution for you.
Challenge 4: Lack of Data Professionals
To run such modern technologies and big data tools – companies need skilled data professionals. These professionals include – data scientists, data analysts and data engineers experienced in working with the tools and making sense of enormous data sets. Organisations generally face such problems because data handling tools have evolved speedily. But in most cases – the professionals still have not.
Solution:
Now that companies have started investing more money in recruiting skilled professionals will also need to offer training programs to the existing staff. Another essential step that organisations should take is to implement data analytics solutions powered by AI. These tools can be run by professionals who are not data science experts but have basic knowledge about the same. It will help the organisations save quite a lot of money on recruitment.
Challenge 5: Securing Data
Securing the enormous sets of data is one of the most critical challenges of Big Data. Companies get so busy understanding, storing and analysing their data sets – that they push data security for later. But it is not a good move, as the repositories of unprotected data will attract malicious attackers.
Solution:
Nowadays, companies are recruiting more cybersecurity professionals to protect their data. According to a Cisco report, the Cybersecurity field has gone up to 74% over the past five years and they have estimated that there are currently 1 million unfilled cybersecurity jobs worldwide. The other essential steps taken for securing data include – Data encryption, Data Segregation, Identity and access control, implementation of endpoint security, real-time security monitoring and use of Big Data tools like IBM Guardium.
Conclusion
To resolve the critical business challenges that Enterprise will face in 2021, you should analyse your company’s problems and what processes you can follow to improve with the available resources. Thoughtful planning, commitment to the strategy, and flexibility are the things that will help stay competitive and stay ahead of time.
