Data analytics has always been associated with Big Data and large corporations. During the early days of analytics for business, only the big players were involved and the strategies and actual benefits have rarely been released to the public. While most business owners believe in the value of data, until recently only 30 percent of them have actually implemented strategic data program and enjoyed benefits from data. Likewise, the majority that have data, have not routinely used it to make business decisions. Rightly or wrongly, prohibitive cost has also been associated with business data analytics.

How small business can benefit from data

For small businesses, size and cost are not limiting factors in getting insights from data. We are primarily motivated by the large community of small businesses who contribute significantly to economy. In many countries, including our own, small and medium enterprises are important revenue generators and the government have policies and incentives that promote growth of this community.

In the following, key factors that determine the success of insight driven initiative for small businesses will be deliberated. If it can motivate business owners to use data, then the intent of driving the single most important takeaway message that all businesses can benefit from data is achieved.


For small businesses that can only allocate small investment in using data for growth, insights must be very specific, clear, simple and easily translated to actions and business decisions. The insights must focus on addressing existing pains and achieving immediate gains. Most importantly, the insights from data must directly support key business objectives that include getting profitable, keeping productive resources, attracting and retaining customers and sustaining growth.

Data can be used to help achieve all of these objectives, but small business owners must focus on the most strategic objective. For example customer acceptance of their products or services could be most strategic objective to focus on. On the contrary, focusing on cost and quality of supplies could be the most strategic for other business owners. Consequently, specific datasets need to be produced so that specific insights can extracted to support specific objectives in brand loyalty or supplier performance. For example, data related to customer satisfaction, complains, feedbacks, suggestions, and preference must be collected to gain insights in customer acceptance. Whereas for managing supplies, data concerning materials quality, defects, rejects, delivery time, cost and quantity can be used to predict demand and cost.


Small businesses presumably can only afford minimal datasets to sufficiently support accurate model and correct insights. Since small datasets have been widely and successfully adopted to benchmark machine learning algorithms, it gives assurance that the same can be said about small business data. This is further supported by reproducible experimental observations that suggest random samplings of fraction of the original datasets give the same predictions and insights, albeit with poorer but acceptable performance parameters. These findings strongly support that small but good quality data can be more valuable than useless Big Data. It can be further recommended that small businesses focus on making many immediate small wins instead of one late big win.

Businesses with small investment in business data must focus their resources on low hanging targets. This includes key enablers that would help achieve critical milestones. In this approach, small businesses should seriously consider utilizing existing data and models that suit them, and use it to predict and gain insights for their business cases. As far as data is concerned they only need to build their unique business cases. While this in not the only low cost approach available for low investment, it can be successfully implemented in the presence of experienced hands.


It is presumed that most small businesses won’t be hiring any data scientists. Therefore, in order to do insight driven business, technical support and strategies must come from external sources. It must be emphasized that all these must be technically sound and available at affordable cost. As pointed in the above, in most countries the significant size of small business community and its huge impact on economy justifies business initiatives that propose to solve their data problems.

In a data-as-a-service approach, data analytics companies can fully support strategic business objectives, help small businesses to focus on strategic small data, reuse available data and models, push low cost cloud infrastructures and aggressively make many quick wins in low hanging targets. This approach allows a common low cost engine to be repeatedly used to produce insights for the whole community – in a data-in-insight-out fashion, and without customized features or any tweaking.


Instead of looking for trends from the past, insight driven approach uses Data Science to drive into the future by looking ahead. Model, prediction and interpretation lead to strategic information or insights that open the possibility of discovering business opportunity or making correct business decisions. Using this approach, businesses not only can predict the acceptance of their customers or the performance of their suppliers, they can also provide explanation to the predicted outcomes.

For example, customers’ decision to switch to available options could be driven by different reasons. For customer retention program to be effective, it is important to know these specific reasons, and it is also useful to know if multiple factors are affecting customer’s decision.

It might happen that small businesses are on the right track in their insight driven journey, but it is not so apparent to them that they have gotten benefits from data. This is when conviction in using data for growth needs to be highlighted – patience in pursuing business goals and strategic objectives can only come from deep conviction.


Insight driven journey must be initiated with absolute clarity – the objective it supports, external technical team, cost and dataset required. The goal is to make quick wins including achieving key milestones. When it starts working, time will make it even better. Longer time means more data, and better datasets mean more accurate model and better insights.

Data-to-action process is highly scalable and in the long run it gives more benefits at a fraction of the cost. This is in keeping with the logic of business growth, and this is the primary mechanism of how data contribute to business growth. As business scales up, business owner keeps its original intent and objectives. Due to scalability, successful small businesses do not need to change its insight driven methodology . Even with additional resources in hands, there is no need to copy data strategy from large corporations.

In a truly sustainable approach affordable data solutions are pushed to large communities that can benefit from data to improve their businesses. They in turn can be more profitable taxpayers, contribute to their ecosystems and other communities – all through data.


Small businesses represent a huge community of employers and taxpayers whose growth greatly impact the economy. Addressing their needs is business opportunity to data companies who can partner with them by pushing affordable insight driven solutions. A data-in-insight-out solution requires no tweaking, and external technical support can focus on business objectives and reusing available data in aggressively winning low hanging targets.

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