25.08.2022 | admin

How to Build a Data-Based Business Strategy in 2022

Data-Based Business

The use of data has climbed to the top of the corporate agenda, but few companies know how to incorporate it successfully. In an EY survey, 81% of organizations agreed that data should be at the heart of every business decision.

But the survey also revealed the data is not used to its fullest potential — so far, it’s limited to addressing specific business issues. Instead of leaving data scattered in silos, let’s see how you can extend the usage of data to cover your entire business framework and culture.

What Does It Mean to Have a Data-Based Business Strategy?

A data-based business strategy rests on two pillars—a clear plan for how to gather/use data and having the analytical capabilities to support it.

The common trap of handling data is collecting it and then asking what it can do for you. This doesn’t just delay all subsequent processes; it also minimizes the value of data. Instead, a data-driven business has a guide plan that defines the people, processes, and technology to put in place before data enters the picture.

Other traits of a data-driven business include:

  • Reliance on observable facts and trends rather than gut feelings
  • Real-time processing and delivery of data
  • Flexible ways of organizing data
  • An enterprise-wide view of data (also known as the democratization of data)
  • A test-first mindset for hypotheses and assumptions
  • Automated processes
  • Robust data security and privacy protections

What to Consider When Building a Data-Driven Business Strategy

Data doesn’t become a strategic asset on its own. Any organization looking to get ahead with their data strategy should consider the following:

Quality of Data

It’s unlikely that an organization will have all the data it needs or that the data it has is good enough for further analysis.

Before including any information in the data set, you need to establish whether it has the right level of detail and is relevant enough for your objectives. If you want to be granular with it, it can also be helpful to assess it with 6 main dimensions of data quality—accuracy, completeness, consistency, validity, uniqueness, and timeliness.

Business Needs

No one type of data is inherently better than the other. So, depending on the business need, certain types of data will be more suitable than others.

Another thing about data is that it only has real value if it’s tied to a specific business need. So, start by defining the key challenges and business-critical questions that need answering.

Don’t try to sync up goals in different areas of the business, even if they’re nestled into each other, such as marketing and sales. You can look at them as part of a company-wide data plan. But having distinct goals in mind is essential.

Tech Requirements

Becoming a data-driven company requires much more than investing in technology. But you’re unlikely to achieve meaningful results without doing so.

Before investing though, answer the following questions to figure out your company’s optimal tech requirements:

  • To what extent does the existing technology meet your current and future needs?
  • Does the organization have the resources for an on-premise data warehouse?
  • What data gaps need to be filled? Do they require a new source system?
  • How will the organization provide access to data?

Important note: the latest technologies are not always the best. In some cases, it makes sense to downgrade and maintain a flexible and scalable data architecture without a steep learning curve.

Percentage of organizations leveraging analytics

Governance

Data governance and analytics make each other stronger.

A governed data-driven environment offers systems for gathering, using, and storing data. So, a decision maker will receive data through approved channels and methods and in a way that allows the data to be gleaned easier.

The Seven Key Foundations FOR Modern D&A Governance

Strategies that account for data governance help prevent data misuse. According to Gartner, the value of data can “only be realized if the right people use them to make the right decisions that drive the right business outcomes.” The same survey also showed that highly resilient organizations focus on the clarity and effectiveness of IT governance more than fragile organizations.

Employees

A data-driven business strategy is likely going to change a lot of employees’ daily tasks. Business leaders will need to anticipate and prepare for these changes in advance. It may take the form of data and analytics training, new recruits, reassessment of organizational structure, and more.

You may find roadblocks to implementing data-driven decision-making at different levels of the organization. The roadblocks may also have different causes. Some people will not have the level of data literacy required, some will not have the ability to think differently, and others will not have either. Whatever the case may be, build internal momentum before implementing the new business strategy.

Roadmap

All the previous considerations will bring you from where you are to where you’d like to go. But you’ll need a plan to know how to get to the final destination. In other words, you need a plan that breaks down the big picture into manageable chunks.

With a roadmap and a timeline in hand, you’ll know whether you’re on track to becoming data-driven and how far you’ve come. This will be useful for stakeholders and management, as well as low-level employees.

Building a Data-Based Business Strategy: Step-by-Step

Building a Data-Based Business Strategy

Data-driven companies are built collaboratively, requiring sufficient time and energy from all key participants. But unless data efforts are aligned across the organization, the time and energy invested will not support the overall mission. We suggest following a set structure for data initiatives, which is described below.

Source Data Creatively

Companies can encourage a more comprehensive look at data by choosing a variety of data sources:

  • Internal data. Records of customer interactions, information in customer profiles, email marketing metrics, and online activity.
  • Third-party analytics. Data from suppliers, resellers, channel partners, regulators, and other stakeholders.
  • External data. Search queries, demographic information, trending keywords and subjects, and financial trends.
  • Open data. Government data, health and scientific data, and social media.

Decision makers and managers may not be able to grasp the potential value of certain types of data—for example, nontraditional, unstructured data in the form of conversations, photos, and video. Data sources should be vast enough to prompt broader thinking; i.e., “If we had all the information we need, what decisions could we make?”

Get the Necessary Support

Legacy technological structures may stand in the way of innovating data sourcing, storage, and analysis. The way data is currently organized (siloed information) leaves many participants behind. What’s more, traditional IT capabilities are often incapable of managing unstructured data. This is the first part of the equation for unlocking the data potential—introducing better tech.

The other side of the problem is closing the gap between tools and people. Data should be clear for people on the front lines who are not data experts, which should be taken into consideration. The solution may require programs for upskilling employees and organizational changes.

Embed Analytics in Decision Making

The third step of building a data-driven strategy is ensuring the right people have easy access to the resources they need. Whether it’s marketing, risk management, or operations, any frontline manager should be able to use big data on a daily basis.

But simple and usable models are not enough to make data-driven insights a part of daily operations. There needs to be training and incentives. Some data-driven companies also introduce metrics to reinforce behavior.

There might be friction because many teams have relied on guesswork to solve problems and identify opportunities for years. But analytical skills and data literacy simply need more practice (and the necessary support in place).

What a Data-Based Strategy Can Cover

Theoretically, every company can get their hands on advanced data analytics tools. The difference between any company and a data-driven company is the strategic use of the insights. Here are some of the areas that can benefit from an advanced tech stack:

Customer Care

You can employ a data-driven approach to provide a seamless on-demand customer experience. Data can provide the tools and insights to enhance customer mapping, analyze their buying behaviors, predict trends, gauge the success of new ideas, and do a lot more. You just need to know how to pull these conclusions.

Your actions will no longer be just responsive to customer interactions – they can be proactive and predictive.

Manufacturing Resilience

Building resilience into supply chains at the planning stage is more effective than responding to disruption. Similar to customer interactions, organizations must plan ahead for the risk of severe issues.

Data can inform a number of decisions—from material acquisition to workforce needs and market demand. And if you make a miscalculation at any step, you will be able to trace the defeat back to its source.

Time-to-Market

Introducing a data-first mindset to product development can change the entire cycle. Even before the product is put into production, data can inform decisions around product specifications, pricing, targeting, etc.

Deloitte found that a data-driven approach can shorten time-to-market by up to 20%. While data doesn’t always reduce time-to-market directly, it can do so indirectly. For instance, with data-based insights on market trends and competition, the product team can develop ideas and make decisions faster.

Resource Optimization

Data provides tangible support to managers, helping them plan resources accurately and ensure competent allocation.

For example, they will estimate project demand and utilization and come up with an adequate plan of action ahead of time. This can be done based on documented data assets of previous projects and other, time-sensitive variables.

Moreover, data assets can be used to assess resource-related risks and avoid potential bottlenecks.

Examples of Data-Driven Companies

Here are the companies utilizing new technology and information assets to unlock greater opportunities and serve their customers better.

Google

Google Manager Behaviors

Google, a famously data-driven enterprise, has created a People Analytics Department and a group called the Information Lab. The goal of this initiative was to start making HR decisions with data. They asked social scientists to find out “Do Managers Matter?” in the codenamed Project Oxygen.

They analyzed performance reviews and employee surveys and created new data sets through interviews.

Among the concrete actions that followed this analysis were:

  • A twice-yearly feedback survey
  • Introduction of the Great Manager Award
  • Revision of management training

Netflix

Netflix

Manager of data platform architecture Jeff Magnusson and engineer Charles Smith shared the brand’s data philosophy:

  1. “Data should be accessible, easy to discover, and easy to process for everyone.
  2. Whether your dataset is large or small, being able to visualize it makes it easier to explain.
  3. The longer you take to find the data, the less valuable it becomes.”

With these rules in mind, Netflix uses data to personalize every aspect of user experience—from color breakdowns to show recommendations. The team also continuously measures how each tweak changes viewing habits.

DBS Bank

DBS Bank

DBS Bank made analytics everyone’s job. But before the company was able to strengthen its digital capabilities, it needed to prepare its employees. The organization-wide upskilling Data Heroes program was based on the use of data to do the following.

  • Address business challenges in a highly-structured fashion
  • Identify business opportunities
  • Facilitate closer collaboration with data scientists and technology teams
  • Create a strong data culture

The comprehensive training curriculum catered to different knowledge and data literacy levels, and DBS transformed into a data-driven organization in under a year.

Key Takeaways

A data-driven business approach is not a simple or short-term undertaking. It is a long-term plan that takes a holistic look at data, the context around it, and the organization as a whole. Keep all critical pieces to the puzzle in mind as you look to integrate data into your decision-making and have it support your business goals.

Executives don’t need to implement massive changes to become data-driven. It can be done through smaller changes—in the ways the company sources data, builds models, and embeds a data-driven culture.

It’s also worth noting that the technology for managing and analyzing information is continuously evolving. But as long as companies concentrate on the core skills of using data, they will be able to keep up.

Before committing to a suite of tools or doing an overhaul of your data strategy, consider talking to an expert. This will give clarity and vision to make the most out of your data.