Today, humans are creating more amounts of data than ever before, and this has introduced us to a new thing called Big Data. Big data is a technology that deals with large amounts of user-generated data. Professionals in this field use the gathered data to generate business insights and metrics that help to take the next decision in running a business.
Many startups work on big data technology, and they face newer problems every day. Today, we have consolidated four such significant data challenges that every startup faces. These are general problems and are not confined to startups only, so if you are a big organization planning to start big data, you might face these problems too.
Worry not; we’ll also cover the solutions to these problems as we advance and discuss the issues individually. So make sure to read the post till the end.
Big Data Challenges Facing Startups and How to Solve Them
1. Lack Of Talent
Big data is a relatively new technology, and there is a lack of talented professionals in this field. There is a massive demand for talented big data developers and workers, but the supply is very scarce.
Jobs in big data technologies are some of the highest-paid occupations in the Software and IT industry, but the lack of knowledge and resources has created a scarcity of personnel. Data will power the future industries and the companies with better data professionals will excel.
The humongous amounts of data that is created every minute need to be handled in a way that big data becomes helpful in the business and unlocks growth. But this will be possible only when there is a good supply of talent and professionals.
As a startup, you may be in tight budgets for hiring professionals, and this will act as a barrier for you to access talented big data professionals. Large organizations have enough money to lure in better-talented professionals. Due to their benefits, people like to login in first, which is another challenge for you as a startup.
Every big data startup needs to step up its scouting game and find talented individuals early on to solve this problem. When you get access to qualified big data professionals, it becomes easy to manage your processes, and you can hire them at less salary. Even though you are doing lateral hiring, you should go a step ahead to find the right candidate and not just rely on their degrees. Doing this will open doors for the right talent to your company, and your startup will unlock growth.
Another way you can solve the challenge of talent is by providing ample learning opportunities and training to everyone working in the big data department. Once you inculcate the culture of learning and progress, you will attract talented individuals to your startup, and your personal problems will vanish.
2. Data Spread Across Sources
Another common challenge that all big data startups face is the problem of data spread across sources. Today, businesses use multiple tools to gather data and store them in different forms. Due to this, gathering the data becomes a big problem, and as data access is difficult, the task of deriving insights becomes challenging.
Startups use different products in different departments, and this spreads business-critical data into multiple sources. Due to data spread across various sources, it almost becomes impossible to use it, and there is no benefit of leveraging big data technologies.
To solve this challenge, there is only one way, and that is to unify the data sources. By unifying the data sources, we mean that though data is collected at different checkpoints, there should be a commonplace where all the data should reside. Doing this will provide you with a large data store of your business data, and accessing and deriving insights from them will also become more accessible.
Another big challenge that every company that works with big data technology faces is data management.
In the previous point, we saw that when data is sourced from multiple places, and it is stored at different locations, too, there are many issues that take place.
Data management includes aspects like storing, retrieving and securing data. When large amounts of data are used, the security of data becomes complicated and retrieving and using it is also a problem.
You cannot iterate over every data item in big data storage and find your relevant data. It will take ages, and that’s why proper data management is needed.
A data store that is not secure is the worst thing, every data point is vulnerable to attack, and storing data in such big data systems is an invitation to death.
The only solution to manage data in any big data startup is to have streamlined processes and models that everyone should follow. To make data management more straightforward, you should always prioritize data cleaning and maintain consistency in the data. Data warehouses with cleaner data are easier to manage, and securing them is much easier as the same types of files can be secured in the same way.
While retrieving data, you should ensure that speeds are fast and the cache is large enough to store large amounts of frequently accessed data at one place. A good cache memory and CDN will decrease data retrieval times sharply, and it will be the best investment in your big data startup.
4. Converting Big Data into Insights
Every startup wants to integrate big data, but hardly a fraction of them knows the correct way to leverage it in their business.
Big data warehouses and databases contain humongous amounts of unstructured data in the form of text, files, images, and other graphics. Due to the unstructured form, it is pretty hard to derive business-related insights out of this.
The only solution to converting big data into insights that power business is by hiring talented big data developers and tools. With the right tools in your arsenal, you can make way through large amounts of unstructured data with a breeze, and insights will be out soon.
Every big data warehouse has many metrics inside it, and with the number of metrics, you might get confused about which to choose. So before you start out the journey of finding data insights, you should have a predefined metric to look for in the big data store. Doing this will save you a lot of time, and you’ll stay away from confusion.
Moreover, you can develop methods and processes to complete the exploration if you always go behind one metric, and it will be a lot easier to get business insights.
By now, you know the top four big data challenges faced by all startups, and you also know the solutions to them. So, if you face any of these in the future, you can quickly steer out by using these solutions and save money on startup problems.
