Data analytics is nothing new. For decades, companies have always used data for planning, marketing, product development, and many other purposes to grow their business. The difference in today’s global and highly connected world, however, is that the data gathered today is larger in volume, has more depth, and can be used for an increasing number of purposes.
With the speed and quantity at which data is being generated and gathered, large companies and multinational corporations have capitalised, building infrastructure to ensure that precious information is not wasted.
Small companies, however, do not have the same luxury.
Because with new opportunities to utilise data comes cost, and the cost of data analytics – in today’s globally connected businesses – is a cost that small companies may not be able to afford.
Cost is one of the main hindrances small companies encounter regarding data analytics. And given that there is already an awareness of why data analytics is important, the conversation now shifts to how exactly can small companies utilise their data.
We look at the cost of data analytics in two aspects: infrastructure and manpower.
Costly building, continuous maintenance
Small business can go one of either 2 ways: build their own or utilise off-the-shelf data analytics systems.
Building a data analytics system in any platform requires a significant amount of resources for design, coding, testing, and documentation. For small companies, it’s like building an entirely separate product to work alongside their actual product to collect and process data.
This means hiring experts and investing on tools that could add up to a six-figure sum; a sum that small businesses are not likely to have.
Even if the infrastructure is built, the cost goes beyond that. The cost would revolve around consistent investment on manpower, firmware, and hardware updates.
While a small business may avoid maintenance costs if they avail of off-the-shelf data analytics services currently available in the market, said service is also quite expensive. The price of these off-the-shelf data analytics systems is due to their power – they have the capacity to capture and process large amounts of data, which is all great for large enterprises but is not economically viable for small businesses.
For example, for a small business, the most frequent use of said data analytics system is at most twice a week, and would unlikely be utilising the full capacity and features of the system but would still pay the full cost. While data is important, the amount of data that small businesses gather and process, as well as the type of analysis that they need which are simpler compared to global enterprises, does not justify paying for the full cost of the system since they might only utilise 10-20% of its capabilities.
Expert and expertise, or the lack thereof
While analysing data is not a new concept, the quality, quantity, and functions of data that businesses now amass, as well as the infrastructure used to gather said data requires someone with a deep knowledge of data analytics. In this case, human resources is the most significant cost as it is needed both in the building and maintenance of the infrastructure.
Data analytics is complex and therefore requires real know-how and experience of specialists. In a 2015 report, professional platform LinkedIn said that out of 236 million profiles, there are only about 19,400 data scientists worldwide.
This scarcity, as well as the training and experience needed to attain expert status in data analytics, is also part of what fuels the high cost of such talent. As an illustration, the median annual salary of an entry level data scientist in Singapore is over US$50,000. Expert level data analysts can go up to an average annual salary of US$80,000.
For a small business (or a startup) that have a limited runway of cash, hiring just one data analyst is a huge expense they may not be able to afford.
While most will argue that the benefits of data analytics outweigh the cost and therefore companies should invest on it, the reality is that most small companies simply do not have the resources to spend on data analytics tools, infrastructure, and talent.
The result? They either rely on the simplest available data analytics tools like Google Analytics or simply don’t analyse their data at all.
For small businesses without hundreds of thousands of dollars to spend on data analytics systems they won’t be able to fully utilise or hire talent they can’t afford, a solution could be found in blockchain technology.
DATAVLT provides small businesses with an agile, on-demand end-to-end data management platform for making meaningful sense of data backed by artificial intelligence and machine learning capabilities.
This means that with DATAVLT’s platform, the analysis process is streamlined as the system processes and analyses their data for them, and they only need to pay for the service that they use. Another important note is that DATAVLT gives them access to an ecosystem that allows them to buy and sell relevant data from other data owners – small businesses now has access to relevant market data without resorting to gathering themselves as well as be able to earn off their own gathered data.
Currently, DATAVLT is looking for partners who are interested to be a part of their Pilot Partners Program. For more info, reach out to datavlt.com/pilot or email – [email protected]
Disclosure: This article is produced by e27 content marketing team, sponsored by DATAVLT
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