Why Data Strategy is necessary for Small Business

The more energy, the faster the bits flip. Earth, air, fire, and water in the end are all made of energy, but the different form they take are determined by information. To do anything requires energy. To specify what is done requires information. Seth Lloyd, Professor of Mechanical Engineering and Physics at MIT 

Business has undergone digital revolution. Internet has become ubiquitous. Social media ecosystem has pervaded our society. Global pandemic has hastened this transition by forcing businesses, big and small, to take refuge in digital space for conducting almost all of their business operations. The crisis has profoundly altered our daily lives raising havoc on service  economy. Small business community are particularly brutalized; out of every three small businesses one has gone non-operational. Rest are compelled to spend their precious capital to reinvent themselves in digital environment. Wether we like it or not, our economy has irreversibly transitioned to digital space.

Future is not all that bleak for the business in the post pandemic world, now that we are on the cusp of managing this crisis with promising news in vaccine development.  Data these organizations have generated from their online and digital activities can be blessing in disguise. With carefully implemented data strategy these businesses can ultimately glean insights on their operations and make more informed business decisions.

Why Do I need data to run my business?

Let’s face it, business is a complex maze of decisions, some less critical than others. There are customers to satisfy, competitors to fend, public health crisis to survive… Who has time to worry about data? Besides, data could be confusing. Not all of us involved in business are statisticians, you know. We have been trusting our guts and experience, and we have survived! We know our business.

Best among us are tempted to seek comfort in this line of thinking. We are follies to our biases. What is familiar is what we gravitate to, and what we cannot see, we are unable to comprehend. But we make best decisions when we are informed about our choices. If there is a possibility of optimizing your business processes with a little investment that can have huge impact in savings, if there is a possibility to manage lean inventory by maintaining efficient supply chain, if a completely new insight on costumers interest can make way for higher customer satisfaction, and if an analysis of sales data combined with data-driven marketing strategy can prove beneficial for attracting new customers, then why not?

In fact, properly curated data about your business activities can reveal a lot about your business. Besides, you are not just relying on one-time measurable and quantifiable metrics to make decision for today, you are also positioning yourself to make better decision in future by analyzing the impact of your current decision.  Moreover, depending on the business requirement, data driven feedback to business operation can be made available almost instantly through real-time transactional data processing system.

What is data strategy?
In short, data strategy involves a plan to collect, store, secure, and ultimately use data. But, to implement a successful data strategy it requires business to take data seriously. It requires leadership team to embrace data in their decision-making process. It requires a cultural shift in organization that views data as asset for making informed business decision, and not as storage and security liability with no intrinsic value.

Most of the world will make decisions by either guessing or using their gut. They will be either lucky or wrong. – Suhail Doshi, Founder of Mighty and Mixpanel.

One way to kick-start this cultural shift is by integrating data in your business strategy. What does this mean? Well, it means that the business decision you make should reflect the insight you gained from data. Any strategy, no matter how good it looks on paper, can only succeed if it can be executed. The industry’s data strategy experts have complained that the organization often fails to implement their data strategy successfully because the leaders have not tried enough to cultivate and promote data culture in the organization.  The leadership structure are also blamed for their reluctance to execute data-driven decision that requires course correction, as that could be controversial for organization new to this culture and requires strong leadership stance to enforce that cultural practice.   Sometimes the strategy could fail because the right expertise and resources  are not put in place or not in sync with the business goal. Bottomline is that the leadership need to own the strategy and make themselves accountable for it.

Do I have the data I want?

Once you understand what business question it is that you need data to answer, then you can work backward to find out what data it is you need.  For example, if you are a seed manufacturing company and you want to know which crops to plant for lucrative use of your farming land, and you are not sure which seed has given you the best return on investment (ROI), you start with marketing data for your seeds to see how much you invested on each of them,  and  take the product sales data to find out how much revenue each product has generated. These two dataset combined will provide you the ROI on each product. This could make your most pressing decision about your product pipeline a lot more informed!

Initial insight gained from data are often incomplete.  Almost always, the initial analysis would trigger dialogue that leads to more robust analysis. To continue with the example above, the ROI derived from sales and marketing data prompted manufacturing team to question the ROI metrics without their side of data. We don’t know how important manufacturing KPIs such as the crop maturity rate, time-to-bloom, and genetic makeup of the seeds are for the ROI could be, but these are important activities seed company perform and therefore make sense incorporate in your ROI assessment. In the end, even if  it did not have a huge impact, you are probably glad that you analyzed your business process.  Either way you are making a more informed decision with the updated ROI.

Bright Technology Outlook
Not that long ago, technology to implement robust data strategy was not feasible for small business organization. Organization had to depend on traditional data warehouse for staging data, transforming data into consumable information, and securing the data from abuse.  These data infrastructures were costly to build and costlier to maintain. Nowadays you can pay for affordable data storage service in cloud. Suddenly, matured and secure data platform has become affordable for small business!

Hopefully, I am not loosing you with my ramble here because I was trying to make a point that data discovery is an itirative process. The more you analyze data the more insight you gain,  and that prompts you to ask more intelligent questions which may require you to plug in more data in your analytical system.

What data infrastructure do I need to process my business activities for data analysis?

Before I ramble on and bore you to death, I would suggest you check out our  experts guide on specific tools and technology that will assist you in making this decision, and spare you my post in return 🙂

Broadly speaking, there are two stream of technologies, sometimes overlapping,  that provide data infrastructure to extract, transform, and analyze disparate dataset. You may have heard about Big data and how it is transforming not just business but our society with predicting learning and Artificial Intelligence applications. This technology is revolutionary. It tackles large amount of unstructured data, such as video footage, images, social media data feed, and applies data algorithm (steeped in the  academic rigor of data science) to find patterns in the unstructured mess.  So scientific!

If somebody tortures the data enough, it will confess anything. Paolo Magrassi, former vice president, research director, Gartner.

Thanks to robust managed service cloud platforms and maturing data science field, the skill and technology are more accessible and affordable even for small business industry.

Then there is traditional Data analytics  and Business Intelligence (BI) technology for analyzing structured and sometimes semi-structured data.  BI is an umbrella term that includes all tools and technology required to access and analyze these types of data. For example, it includes infrastructures such as Customer Relation Management (CRM) softwares that can pull data from different transactional systems and feed it to the tried-and-tested universe of data warehouse (curiosity alert! also check out datalake and how it differs in concept and application from Data Warehouse – plugin here) and Relational Database of Management System (RDBMS), where entity relationship means something (pun intended). The data from warehouse is then integrated with the reporting tools for precise data analysis.

Business at times requires a more time sensitive, present focused analysis of the business operation. For example a customer service department of a company addressing real time delay in call center operation using real time data. In this scenario Online Transactional Processing System (OLTP) is suitable. Other times, business wants to analyze the data covering long timespan to see historical trends in data. The term data analytics is in fact is attributed to this kind of analysis where we build a data repository to keep historical data that are mostly preaggregated for data analysis. The system that allows such analysis possible is also known in data industry as Online Analytics Processing (OLAP).  By analyzing the past data business can often detect not-so-obvious business trends that can help them optimize their business processes.

Statistics are like bikinis. What they reveal is suggestive, but what they conceal is vital – Aaron Levenstein, business professor at Baruch College.

There is a vast array of products and services at offer to itemize more affordable and applicable data analytics solution for small businesses like yours.  Data is the currency of today. Leading industry giants like Google, Amazon, Facebook, are all reaping benefit from your data—data monetization is real. Let us help you to cash it!

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