How SMEs Can Benefit From Data Science Projects

Posted 2017-09-28T Posted by Tom

The adoption of data science strategies by smaller businesses is no longer a rare or obscure practice. Many smaller businesses that adopted data science early on are now benefiting from utilising their data and have a substantial competitive advantage, in some cases even building substantial barriers to entry. One of the key challenge that SMEs face is how to develop a more established data science strategy to help them grow to the next level.

Data Science Brings Substantial Bottom-line Benefits

Data science strategies can have a positive impact at every stage of business operations. This includes things like price optimisation at scale, customer classification and analysis, risk assessment and product value. The scale of such work should not be underestimated; however, the results can offer have an immediate impact on the bottom line. One of our clients - Parts Alliance Group - for example, recently adopted data science to evaluate 20 million historical transactions in order to create the most efficient and modern pricing engine. The results of the undertaking should also not be underestimated as the firm is expected to boost revenue by £6 million - an impressive return on a five-week data science project undertaken by for four junior data scientists.

It is currently estimated that up to 35 percent of businesses are adopting data science in their business model. Larger companies find adoption of data science much easier than smaller firms, where less inclusion has currently been enjoyed. SMEs have received the same hype relating to the potential of data science as that of bigger firms, but are less likely to take the leap. With smaller skillsets and lower budgets, smaller businesses are more reluctant to invest and often have less time to thoroughly research the requirements and likely returns.

This is frustrating for data science experts as SMEs are the foundation for a successful economy and as such, these are the businesses that stand to achieve the most from the revolution. Reassuringly, data science is not a laborious or costly undertaking and those SMEs who have got on board are quick to report the seamless inclusion of more advanced practices alongside the substantial profitability and efficiency growth that results from the adoption. SMEs simply need to know the problem that they want to address with data science in order to begin. Bernard Marr, a data consultant, confirmed this in a recent article, stating,

“In many ways, big data is suited to small business in ways that it never was for big business – even the most potent insights are valueless if your business is not agile enough to act on them in a timely fashion. Small businesses have the advantage of agility, making it perfectly suited to act on data-derived insights with speed and efficiency.”

5 Tips For Managing A Successful Data Project

For managers to utilise their data effectively and reap the rewards of data science offerings, we recommend the following:

1. Identify the business need or problem Data science is able to provide an abundance of information for a business to gain maximum benefit, the problem should be identified so that the solution is made clear. Once an objective is set or a business need is outlined, data scientists can offer a bespoke focus in order to achieve quantifiable goals.

2. Form a team Managers need to determine whether the data science project can be staffed from within or, will work need to be completed by an outside team. Thereafter, a budget for staff will need to be decided and it might be possible to mix staff from within existing teams and for them to be accompanied by specialists.

3. Start small Don’t overthink the process or the outcome. Break down the business need or problem into smaller sub-projects and use one of these to examine the integrity and condition of your underlying data; then move on to finding out a proof-of-concept. Scaling-up is easier to sell to the business when you have some concrete findings.

4. Be patient Don’t expect too much too soon. Data science projects take time to strategise, cleanse, analyse and disseminate. In order to ascertain how effective the project is, monitor and measure the end results and outcomes.

5. Respond to the outcomes If the project is successful, expand it and repeat it. Businesses that repeat the process and grow the scale have further successes and gain greater efficiency. Not all projects are successful, but they will nonetheless present opportunities. Failing is the fastest way to learn. Measure everything and respond accordingly.

If you are an SME or businesses and would like to find out more information about how Pivigo can help with your data science needs, please get in touch.

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