Driving Business Operations with Data and Intelligence

In these times, it would be funny, if not outright ridiculous, to find any business owner or manager who still makes decisions at the drop of a hat without harnessing rich insights from a diverse set of information that may have been gathered across the business operations. This information may be drawn from customer interactions, sales transactions, social media, web analytics, supply chain logistics, etc. This vast and diverse set of information collected through various channels is referred to as big data.

Given that every business has some information, it is necessary to highlight what qualifies your data as ‘big’. It comes down to the “Three Vs” – the volume, velocity, and variety.

Before we can refer to it as ‘big data’ it must necessarily be voluminous and beyond the capacity of traditional data processing capacities. The volume could range from terabytes to petabytes of data. There is also the velocity, which refers to the speed at which the data is generated and processed. When you have huge volumes of data being generated and added to your data in real time, then you would also require rapid analysis, which may not be possible with regular data processing capacities. In terms of variety, you would also be considering the different types and formats of data, including structured data (like databases), semi-structured data (like XML or JSON), and unstructured data (like text, images, and videos).

Now, what does big data mean for business owners in terms of opportunities?

The first real opportunity it presents is that businesses can make better decisions. The big data gathered across their operations would give insights into market trends, customer behaviors, and operational efficiencies, and help the business customize its marketing strategies and product features.

Managers can use the analyzed data to uncover inefficiencies in processes and supply chains, leading to cost reductions and improved productivity. The historical data also comes in very handy for predicting future trends and customer needs, anticipating market changes, and optimizing resource allocation.

But to reap these benefits, the business must have first scaled the hurdles of Handling and storing large volumes of data, which often comes at a significant cost. Businesses need the right infrastructure and tools to manage and process data effectively, and none of that is free. They have to get the right manpower and skillset onboard or train their staff to do it. With big data, also comes the responsibility to protect sensitive information and comply with regulations like GDPR or CCPA. Ensuring data security is a critical concern.

When done right, big data has the potential to drive significant business growth and innovation and Netflix’s recommendation engine is a case in point.

Netflix, the streaming giant, has become renowned for its sophisticated recommendation system, which suggests movies and TV shows to users based on their viewing history and preferences. This is a key driver of user engagement and subscription retention, and it is the product of big data.

Netflix collects extensive data on user interactions, including viewing history, search queries, ratings, and even the time spent watching specific content. The platform also gathers data on user behavior across different devices. Using big data technologies, Netflix processes and analyses them to identify patterns in user preferences and viewing habits, and this then guides the sophisticated machine learning algorithms to make the right recommendations.

Why does this work?

If I have a history of watching historical movies or thrillers, I would certainly be more pleased to see similar recommendations in my feed, rather than other genres that I probably have never watched. And in the end, the right recommendations can keep users engaged on the platform for long hours at a stretch. The insights from the user data also influence Netflix’s content creation and acquisition strategies so that they can continue to onboard more of the content that viewers love.

Another case is that of Target. Target, a major retail chain, used big data analytics to enhance its marketing strategies and improve sales. The company analyzed customer purchase data and used predictive analytics to identify patterns and behaviors. One famous instance involves Target’s use of big data to identify purchasing patterns related to customer pregnancies. Target collected data from various sources, including customer loyalty programs, purchase history, and online behavior. The company accumulated vast amounts of data on customer transactions and demographics. The data scientists then used advanced analytics and machine learning algorithms to analyze the data and developed models to predict customer behavior and preferences. One specific model aimed to identify customers who were likely expecting a baby, based on their purchasing patterns like the purchase of prenatal vitamins, maternity wear, and other baby-related products. With this model, targeted recommendations and marketing could be launched at them.

This, of course, led to increased sales and improved customer experience and loyalty while saving costs for the brand since they could now optimize their ads budget. Interestingly, Target’s ability to predict pregnancies became widely known after an incident where a customer complained about receiving baby-related coupons before her pregnancy was public knowledge.

Overall, we can say that using big data to enhance decision-making is a move that favors both the business and its customers.The real issue is that many small business owners think they are too small to begin applying data analysis to get insights for business decisions. This is certainly not true. You don’t need to be a Target or a Netflix to use your data in decision-making.

Whatever the size of your business, you do have some data available. It may not be the enormous datasets measured in terabytes but it is sufficient to begin to guide your decisions at this time. If you have an online retail store, your data may reveal items that customers tend to purchase together, and you could lump them into single packages to reduce delivery costs and encourage more purchases. For customers buying a laptop, you could have a packaged offer that includes a laptop, wireless mouse, and a portable wifi router, for instance. There is so much you can do with the little data you have.

The point is that you should not be making business decisions arbitrarily, based on a hunch or your mood. Decisions about the business should be based on data pooled from the business, however minute they might be.

Engage data analysts and infrastructure that can work with the data you have to provide the insights you need. To get this right, you need to clearly outline what decisions you want to make or what problems you need to solve. As the data analyst works towards that, (s)he can also point out other insights that would help improve your business. As your business progresses, you will, of course, need better resources to store, manage, and process the data. Many cloud services provide flexible pricing, allowing small businesses to pay only for what they use, which helps in managing costs effectively. In summary, don’t wait till you have the big data. Start with the one you have right now.