How data should REALLY be used to drive strategy and differentiation


When it comes to strategy, most companies fail. In fact, 95% of products that go to market do not succeed. The common excuse is failed execution, but this is often just poor accountability from executives for the company not hitting their quarterly targets. Other common reasons business leaders point out are unrealistic plans, the wrong team involved, market conditions, and so forth. The blame game could go on, but it only focuses on how strategy fails and not really on why. 

The strategy itself is never questioned. A strategy shouldn’t be just a goal, but rather a set of clear choices that drive company-wide alignment and focus. Rapidly growing companies are already questioning how these choices are made when deciding their strategies. That’s usually when data comes in, and that’s when companies rush to be data-driven. Most companies recognize the value of data to define strategy, but can’t realize their full potential because they can’t create a systemic approach to a data-driven strategy.


Why companies fail to use data to inform strategy

The first mistake companies make when trying to create a data-driven strategy is how they use their data capabilities, or how the company culture behaves regarding this topic. Organizations that make this error often apply data-driven approaches to some processes or decisions, but not all, thus leaving important decisions out of this loop. They end up creating inefficiencies and poor use of the data throughout the organization and, in many areas inside the company, the business problems still get solved through traditional approaches.

Another common reason for this is that data often has no true “owner” or strategy in place ensuring it’s updated and ready for use in various ways. This shouldn’t be just a compliance issue, but a core decision that affects the whole strategy.

The second mistake companies make is in the data strategy itself. Most of the value in the current data-overload era is in unstructured data, but companies fail to see that or can’t handle it properly. Most of the data that companies use are still organized as in a big spreadsheet, or a relational database, requiring significant time manually exploring and adjusting datasets. Another poor use of unstructured data is that companies must refine data into a structured form using manual, time-consuming and error-prone processes.

To add insult to injury, only a small part of the unstructured data is ingested, processed and analyzed in real-time due to the limitations of the tools adopted by many companies. Datasets are siloed and expensive, making it difficult for non-technical users within an organization to quickly access and manipulate the data they need. Companies then have to make a lose-lose decision about their data strategy, having to choose between two essential factors for successful strategy implementation: agile decision-making or more sophisticated analyses and use cases with data. 


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Characteristics of a genuine data-driven strategy

A successful strategy is preceded by a data-driven culture throughout the organization. Therefore, data should be embedded in every decision, interaction and process, not just in some cases. That makes any decision-making easy, fast and aligned with the “set of choices” that are core to strategy implementation. Moreover, data-driven companies are 58% more likely to beat revenue goals than those that don’t use data in the decision process. Another key characteristic of a data-driven strategy is the real-time delivery and processing of data, making it integrated and ready to use for every stakeholder.

The roadmap for a data-driven business strategy starts with choosing the right data. This provides the capability to have more depth and breadth to the business environment, thus making better strategic decisions. It provides the ability to see the past correctly and make better forecasts about the competitive landscape, market trends and other variables that affect business strategy outcomes. 

Choosing the right data also means being more comprehensive about the business problems and opportunities that need to be addressed. Business leaders also need to get creative about the potential of external and new sources of data, especially when talking about unstructured data.

Once companies have the right data in place to tackle business problems, they need to build the right analytics models to optimize business outcomes. That starts with a hypothesis-driven approach of identifying a business opportunity and determining how the model can improve performance. This approach also ensures buy-in from less data-savvy professionals in the day-to-day use of analytical tools. 


Why embrace technology to create a data-driven strategy?

The truth is that strategy decision-makers no longer have to rely on experience or outsourced consultants to create data-driven strategies. Multiple technologies can help in this process, saving time and money and delivering accurate insights. It’s not always easy to put data to work; the first step is to learn to deal with data from various sources and how technology can help to collect and standardize this data.

The challenge of working with unstructured data on a large scale to create better strategies can be solved with help from predictive systems to artificial intelligence (AI)-driven automation used to organize this data efficiently and ensure the best analytical model to maximize business outcomes. Machine learning, for example, can be considered one of the most important analytical approaches, which can help find connections and trends in the data that human data analysts may not even know how to look for. It can also enable the focus on forward-looking insights, ensuring current data can be converted into real and actionable insight.

To create and implement a data-driven culture, companies should embrace innovative technological solutions as a faster and more assertive way to deal with the metadata world. And they should be able to make decisions based on trustworthy information, accelerating the decision-making process.

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