Data analytics and statistics aren’t historically associated with the strategic decisions that leaders take in small and medium sized businesses. Data analytics has for some years been used in larger organizations and organizations with larger user bases are also benefiting from this, thanks to the use of big data to drive consumer and business insight in business decision making. However, even such businesses can benefit from the large volumes of data that are being collected, including from public data bases. Most decisions in traditional businesses and in small and medium businesses are still taken by leaders who at best have a pulse of the market and a domain knowledge of the business they’re in, but aren’t using the data at their disposal to create mathematical models and strategies derived from them.
When does data fit into strategy?
To answer this, we may need to understand the purpose of strategy and strategic initiatives themselves. In small and medium organizations, the purpose of strategic initiatives, especially the mid- and short-term strategies, is to enable growth. Larger organizations have the benefit of extensive user bases, consumer bases or resources, which they can use to develop, test, validate and release new products and services. However, smaller organizations and medium sized organizations make these strategic initiatives, because their focus tends to be limited to the near term, and in maintaining a good financial performance. Small and medium organizations in modern economies will also seek to maintain leverage and a consumer base that is dedicated and loyal to their product or service journey. The latter is especially true of niche product companies, because they sell lifestyles, and not merely products.
In this context, data fits into strategy in the following key ways:
- Descriptive data analytics allows strategists and leaders to question underlying assumptions of existing strategies
- Data visualizations allow strategists to classify and rank opportunities and have more cost and time efficient strategies
- Inferential data analytics, predictive analytics and simulations allow strategists to play out scenarios, and take a peek into the future of the business
Descriptive data analytics may work with public data, or data already available with the organization. It could be composed of statistical reports, illustrating the growth in demand, or market size, or certain broader trends and patterns in consumption, or demand, for a certain product, service, or opportunity. Descriptive analytics is easy enough to do, and doesn’t involve complex modeling usually. It is a good entry point for strategists that hope to become more data driven in the development of their strategies.
Data visualizations, in addition to being communication tools that provide strategists leverage, could also throw some light on the functional aspects of what opportunities to seek out, and what strategies to develop. They could also help strategists make connections and see relationships that would otherwise not have been apparent. Data visualization has been made easier and more affordable because of powerful and free software such as R and R-Studio. Visualizations are extremely effective as communication and ideation tools. For strategists who look to mature beyond just using descriptive statistics in developing their strategies, visualizations can be valuable.
Inferential data analytics leverage the predictive power of mathematical and statistical models. By representing what is common knowledge as a mathematical model, we can apply it to diverse situations, and throw new light on problems that we haven’t evaluated before in a scientific or data driven manner. Inferential data analytics generally requires individuals with experience as data scientists. Inferential statistical models require a good understanding of basic and inferential statistical models, and therefore, can be more complex to incorporate into data based strategy models. While descriptive and visualizations may not be driven by advanced algorithms such as neural networks or machine learning, advanced and inferential analytics can certainly be so driven.
Data for Short and Medium Term Strategy
Data analysis that informs short term strategy and medium term strategy are fundamentally different. Short term strategy, that focuses on the immediate near term of a business, generally seeks to inform the operational teams on how they should act. This may be a set of simple rules, which are used to run the rudiments of the business on a day-to-day basis. Why use data to drive the regular activities of businesses for which extensive procedures may already be in place? Because keeping one’s ear to the ground – and collecting customer and market information on an ongoing basis – is extremely important for most businesses today in a competitive business world. Continual improvement and quality are fundamental and important to a wide variety of businesses, and data that informs the short term is therefore extremely important.
Data analytics in the short term doesn’t rely on extensive analysis, but keeping abreast of information and the trends and patterns we see in them on a day-to-day basis. Approaches relevant to short term strategy may be:
- Dashboards and real time information streams
- Automatically generated reports that give operations leaders or general managers a pulse of the market, or a pulse of the business
- Sample data analysis (small data, as opposed to big data), that informs managers and teams about the ongoing status of a specific process or product – this is similar to quality management systems in use in various companies small and large
Data analytics in the mid term strategy space is quite a different situation, being required to inform strategists about the impact of changing market scenarios on a future product or service launch. The data analysis here should seek to serve the strategists’ need to be informed about served and total addressable markets, competitive space, penetration and market share expectations, and such business-specific criteria that help fund, finance or prioritize the development of new products or services.
Accordingly, data analytics in a mid-term strategy space (also called Horizon 2 strategy) may involve more involved analysis, typically by data scientists. Tools and themes of analysis may be things like:
- Consumer sentiment analysis to determine the relevance of a particular product or service
- Patents and intellectual property data munging, classification and text mining for category analysis
- Competitor analysis by automated searches, classification algorithms, risk analysis by dynamic analytical hierarchy processes
- Scenario analysis and simulation, driven by methods such as Markov Chain Monte Carlo analysis
Observe how the analyses above are distinct from the more ready information that’s shared with operational teams. The data analytics activities here generally require analysis of data in a rigorous manner, not merely the collection and presentation of available data that fit a certain definition. When data is unstructured and when data science requires the cleaning and visualization of data, the creation of models from a starting point, such as public data, is much more challenging. This is where the skills of well trained data scientists and data analysts is essential.
Data for Long Term Strategy
One narrative that has made itself known through data in the world of business, is that the long term as it was traditionally known, is shrinking. Even S&P 500 companies are conspicuous these days by how short lived they are, and small and medium companies, therefore, are no exception. Successful tech companies boast product and service development cycles of a few months up to a year, and the technology world is therefore unrecognizable from what it was every few months, thanks to innovation. However, there is probably a method to even this madness. The scale and openness of access has made the consumer and end user powerful, and the consumer these days has opportunities to do things with free resources and tools, that could only be imagined a few years ago.
Data informs strategists in such longer term strategic scenarios, typically, five years or more, by helping construct scenarios. Data analytics in scenario planning should account for the following:
- Dynamic trends in the increase of velocity in information/data being collected (Velocity, out of the four Vs)
- Dynamic changes in the type of information being collected (Variety, out of the four Vs)
- Dynamic changes in the reliability of information being collected (Veracity out of the four Vs)
Volume, the other V out of the four Vs, is a static measure of the data being collected at specific points in time, but these above are more than just volume, and they represent the growing size, variety and unreliability of available data.
Data analysis of a more simple nature can be used for some of the analysis above, while for specific approaches such as scenario analysis, sophisticated mathematical models can be used. In small and medium organizations, where the focus is usually on the short term, and at best on the mid term, data analytics can help inform executives about the long term and keep that conversation going. It is easy in smaller organizations to fall into the trap of not preparing for the long term. In the mid and long term, more advanced methods can be used to guide and inform the organization’s vision.
Data analytics as applied to strategy is not entirely new, with many mature organizations already working on it. For small and medium businesses, which are mushrooming in a big way around the developed and developing world these days, data analytics is a force multiplier for strategic decision making and for leaders. Data analytics can reveal information we have hitherto believed to only be the preserve of large organizations who can collect data on an unprecedented scale and hire expert teams to analyze them. What makes analytics relevant to small and medium businesses today is that in our changing business landscape, we can expect analytics driven companies to respond in more agile ways to the needs of customers, and to excite customers in new ways, that traditional, less agile and larger organizations are not likely to do. The surfeit of mature data analysis tools and approaches available, combined with public data, can therefore make leaders and strategists in small and medium organizations more competitive.