Emergence of the Data Science in Small and Medium Enterprises

With bad data costing $3 trillion a year, the value that lies in processing and analyzing data is becoming more evident by the day – and that’s where data science bags the limelight. Data science and business analytics are becoming increasingly prevalent across organizations looking to drive data-driven decisions and improve business outcomes. Do you know? LinkedIn’s fastest-growing jobs today are in data science and machine learning. Data scientist roles have grown over 650% since 2012!


With a 4,300% increase in annual data generation by 2020, there is a huge opportunity for businesses of all sizes. Intel is saving an estimated $656 million a year using predictive analytics across various company departments. Since technology and innovation are strategic priorities for SME growth, data science is a key driver not just to predict market and customer behaviour, but also to make better decisions and increase business agility. While large corporations are already making the most of data science, let’s look at how it can add value to SMEs: benefits, challenges, and best practices.


The potential and promise of data science is just as significant for SMEs. Using data science, SMEs can

  • Generate knowledge from new data sources to drive creativity and innovation
  • Analyse past performance and market behaviour and uncover new insights
  • Nurture alliances creating real-time solutions to challenges
  • Make data-driven decisions at micro-level and witness changes at macro-level due to their position in the economy
  • Get the advantage and flexibility of quickly adapting to changes for improved efficiency.
  • Collect more accurate and detailed information and boost performance
  • Achieve new insights and unearth co-relations, risks, and opportunities
  • Get a deeper understanding of the existing and potential state of business with respect to competition


In a world where data science is propelling organizations to achieve the unthinkable, there are certain challenges that SMEs can encounter:

  • The lack of proper understanding of data science and the benefits it will bring is a major challenge. Clearly, SMEs are not willing to invest in a technology they do not understand.
  • Data security concerns act as major barriers to data science adoption. Since the expertise in IT security is low, SMEs are considerably more vulnerable to data breaches and cyber-attacks. Transmitting large volumes of data through multi‐user and multi‐owner channels and the inability to create an in-house data analytics team results in further loss of control over data.
  • Unlike large enterprises, SMEs have less access to debt finance; in addition, limited financial resources cause SMEs to be very cautious about new investments beyond their exact business scope.
  • A majority of SMEs have few or no in-house data analysts with the expertise needed to approach data science; high set-up costs and poor management support add to the woes.
  • The shortage of qualified data science experts with analytical expertise and skills to understand and make decisions is widespread. Also, while large companies can afford to distribute functions over several persons, SMEs need to look for cross‐functional expertise that is even harder to find.
  • The lack of business cases in the SME market make adoption all the more difficult; with very few success stories, propagation of data science for innovation is even bleaker.


The best way SMEs can benefit from data science is by solving the entire problem using an integrated by an integrated solution, rather than striving for isolated successes in a few aspects. The implications of improvements in SMEs is huge; here are some best practices:

  • Get educated: In order to accelerate the development and implementation of data science, SMEs need to get well versed with the technology and ascertain ways to get trained by specialized experts. With proper insight into data science and the benefits it can bring, SMEs can overcome the challenges of lack of domain specialists, bottlenecks in the labour market as well as lack of management and organizational models.
  • Assess maturity level: The effective implementation of data science in SMEs needs to begin with a preliminary assessment of their maturity level – aspects such as strategic use of data in daily processes and readiness towards the use of data analytics can aid in properly building a maturity model and devising an implementation plan.
  • Build a centre of excellence: The best way to make the data science plunge is by building a centre of excellence team that can provide information and expertise as and when necessary. These small self‐organized teams of data scientists can add value-added benefits at limited costs.


As the world struggles to make sense of large amounts of data, data science can definitely add value to a business by offering statistics and insights across workflows. In order to grow and stay relevant in today’s competitive world, SMEs must embrace technological advances towards the future. Since SMEs have a more flexible IT environment, fewer legacy issues, and can quickly adapt to change, the use of data science may very well become the next frontier of innovation.

At Rubics, we are working on a ground-breaking innovation to help SMEs leverage the power of data science without hiring an army of data scientists. Stay tuned for more details.