Collection, Analysis, and Action - How Data Is Changing Business Modeling and Strategy

Collection, Analysis, and Action - How Data Is Changing Business Modeling and Strategy

Over the past decade, one of the biggest trends that’s impacted almost every industry is the arrival of technology. With the continual advance of new technology, and mass adoption, data production is currently at an all-time high. This presents a unique opportunity for businesses, with this moment being perfect for expanding companies according to data-driven decisions.

Currently, over 92% of all companies worldwide are making active investments in big data and AI, seeing these industries as massive opportunities. Data provides a stable platform for businesses to launch from. Instead of having to rely on emotional or ‘gut-based’ decisions which have no real certainty, data provides a clear pathway forward.

Equally, the extent to which data can be collected from millions of sources simultaneously means that businesses have almost unfiltered access to any information they need. Whether it’s customer information to refine a marketing campaign or industry data to shape their strategy going forward, data has the answer to everything.

In this article, we’ll turn to the core components of data analysis, discussing how it can shape business strategy and its own models for success going forward.

How Does Big Data Drive Progress?

When used correctly, data can become a transformative process for a business. Instead of doing things in the dark, data illuminates the path with the highest statistical chance of success. With this in mind, there are a range of ways that data can drive progress in a business.

From informing a company about which markets they should be focusing on to even guiding marketing teams toward better advertising strategies, data pervades into and benefits all spheres of a business. There are three main areas where data impacts a business:

  • New insights

  • Improves the operational process

  • Customer intelligence

Let’s break these down further.

New Insights

Collecting data from external and internal sources simultaneously allows businesses to build up a more comprehensive image of several areas. Internal analysis can shed light on how a business is allocating resources, while external analysis can compare a company to its competitors. Insights like these allow businesses to more accurately define their movement toward success.

Perhaps a company is seeing fewer sales than a competitor, while still having similar traffic. In this case, the business knows it must shift strategy into a more sales-driven approach, with data directly informing active business practices. Due to how expansive data is, this approach can be applied to almost any area within a company, driving progress from all directions.

By pulling in all of these insights into one location, data analysts can then conduct business model analysis. This strategy attempts to discover if the current model that a business is following is actually the right pathway to take. By fostering a culture that constantly refines and improves its own business strategy, a company can more effectively grow in the right direction.

Improves the Operational Process

A central focus of effective data analysis is in pursuit of improving how a business is currently operating. By examining different departments and operational processes, data analysts can find areas where cost is high, but output or reward is low. By focusing on these areas, data can reveal exactly why a certain process is costing so much.

From there, teams can then work to remedy the area. Perhaps a certain team needs more support to perform their roles actively. Alternatively, the supply chain may be costing a business lots at one certain point. By analyzing this, the company can then find other providers or suppliers and make a switch, saving the business a lot of capital in the long run.

Data allows businesses to continually refine their processes, becoming better over a long period of time. Instead of instant results, this strategy allows businesses to plan better for the future, allocate resources more effectively, and build toward a more successful tomorrow.

Customer Intelligence

A business without customers is, well, a failing business. Customers are the center of a successful company, with how these individuals relate to and perceive a business making all the difference. While satisfying customers and increasing lifetime value used to be a practice that was entirely reliant on talking to individuals and getting feedback, this is no longer the case.

Nowadays, due to the more effective data collection practices that businesses can use, a company can find customer information almost instantly. This helps tremendously when planning and improving customer-facing activities.

For example, two versions of a marketing campaign can be launched - both covering the same content but with slight changes. After collecting data about interactions with the campaign, a marketing manager will be able to clearly see the difference in success. From this, they can extrapolate what customers like more, doubling down on what’s successful, and rectifying what’s falling flat.

Equally, continual site continual site optimizations can be undertaken, with small changes on a website leading to big conversion differences. An example of continual A/B testing on a website, which used customer data to drive a difference in sales comes from the tech giant Google.

Google launched over 50 shades of a blue ‘Buy’ button to see which was most effective with users. Over many months, they discovered the best shade, which led to over $200 million in additional revenue for the company. Simply by looking at the data in front of them, they were then able to change their business style to incorporate this information, leading to a much greater return.

Data Is Only Just Getting Started

Although big data has already come a long way, especially considering how prevalent it has become across many different industries, there is still further to go. One of the best principles for effective data analysis is to have a wide sample of information to draw upon. Due to this, one of the central focuses over recent years has been on improving the tools that data analysts use to interact with their data.

Looking at data warehouses, the efficiency with which these storage facilities can process and standardize data ready for analysis is continuously growing. Take the Druid architecture, for example, one of the leading cloud data warehouse companies. This business has grown exponentially over the past ten years, now offering a comprehensive suite of tools, investigation modules, and impressive processing speeds.

As technology like data warehouses and the tools they offer continues to develop, we’re likely to see the lengths to which data can be taken expand even more. While data is already a leading industry, the progress that could be made in this field is almost unfathomable.

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