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Data moat playbook part 2: leverage data for market positioning

Data moat playbook part 2: leverage data for market positioning

Robert Balaban
Senior Data Engineer

Once you've created a data moat — which means directing your organization's data management in a purposeful, strategic way — it's time to put that information to use. In part one of this series, we addressed what it takes to create a data moat. Now, the focus is on achieving maximum value from it.

Many companies understand that their data has fundamental value but haven't yet implemented the systems to turn that potential into a concrete return on investment. Implementing strategies to turn the corner and convert a data moat into a source of business value should be a top priority.

Tactics for leveraging data moats

The process of extracting value from corporate data can take a few forms. Sometimes, the breakthrough comes from changing an approach to business processes, and in other cases, the key is a new piece of technology or a realignment of human resources.

The ideal approach may depend on a company's size and industry. The amount and type of data in a data moat — as well as the potential use cases for that content — will differ based on an organization's immediate situation.

The following are five prominent focus areas that can help businesses start seeing value from their data moats:

  1. Data-driven decision-making

This is perhaps the most straightforward, classic use case for corporate data. It involves using advanced analytics solutions to draw usable insights from raw information. Of course, this methodology is only effective if there is enough trust in the data to use it as the basis for choices that will affect the company's direction — stakeholders should be comfortable siding with the data over their initial assumptions. 

Data-driven supply chain optimization is a valuable example of this concept in action. Organizations can interpret a variety of inputs from their logistics operations — including real-time inventory levels, customer demand patterns and shipping costs. The resulting insights will help those companies minimize stockouts and overstocking.

  1. Innovative business models

In addition to changing their day-to-day practices based on data, companies can also change their fundamental operations and mission due to input from their data moats. Rather than targeting specific markets or emphasizing particular marketing approaches, this means shifting the way the company deals with its customers.

Data analysis may point to new ways to create value for customers and generate income for the brand. This could mean changing the business's pricing model from single purchases to subscriptions or putting forward the idea of easy, one-click bundled services. Customer data can reveal patterns that even direct surveys might miss.

  1. Partnerships and collaborations

To get maximum value out of data moats, companies can strike up partnerships in which each partner has a piece of the puzzle. This may mean one organization offering up raw information and the other having the capacity to process it, or two businesses each with their own store of data combining those resources in complementary ways.

One potential example of mutually beneficial collaboration comes from retail. The partners are a retailer with a large amount of customer purchase data from its point-of-sale systems and an app company that collects anonymized location data from users who opt in. If each organization shares a portion of its data, while anonymizing and being mindful of customer privacy, the joint analysis can reveal actionable patterns of customer behavior that go beyond the walls of that one store.

  1. Technology investment

While tech tools can't provide value without the right strategic usage, advanced and well-chosen systems do have a part to play in drawing ROI from a data moat project. Today, the most relevant solutions for this role often include analytics tools that incorporate machine learning (ML) and artificial intelligence (AI) to draw intelligent conclusions from large, varied data sets.

Building a data hub in the early stages of data moat creation already calls for tech investment for ingestion, storage and visibility. From there on, stakeholders must continue upgrading their tech resources to make sure analytics algorithms are up to the task of extracting insights from the collected information.

  1. Talent and culture

PPeople are the most important part of a data moat. The culture in place among an organization's employees will determine the success of its data usage efforts. Creating a data-centric culture means encouraging the use of analytics in workflows and giving the team members all the tools they need to live up to expectations — both in terms of tech tools, training and data access.

A company's size will determine its approach to data use culture. Large organizations can hire more people and build specialized departments specifically for the management and analysis of data. At smaller organizations, it's likely that general IT employees will have to multi-task, building out the data moat alongside their other projects.

With the right strategies, tech and people management approaches, businesses can start using their data moats to provide real value. Once data is deeply embedded into companies' processes, they can use it as a competitive differentiator compared to less analytical rivals.

Challenges and pitfalls of data moat usage

To reach the hoped-for levels of data-driven success, businesses have to contend with a variety of challenges. In the AI-driven modern era, with new and innovative information usage approaches constantly emerging, the complexities have only creased.

Some of the headwinds facing companies relate to external factors — largely around keeping data safe and compliant. Other issues are internal and have to do with breaking down silos and keeping up proper usage over time.

Breaking down examples of these problems shows the importance of overcoming them:

Privacy and security concerns

The exact issues regarding correct data storage and use will differ from one industry to another. While almost all businesses today control some personally identifiable information (PII) that requires protection, there are special considerations in fields such as finance and health care. Regulations such as the Health Insurance Portability and Accountability Act (HIPAA) impose specific requirements for data users and dictate penalties for noncompliance.

Considering the new methods of data usage tied to AI, questions around governance are only increasing. For example, will using data to train an AI algorithm lead to data exposure risk or intellectual property issues?

Dealing with data privacy and security is a serious enough part of data moat construction that companies should make sure there are people and policies in place specifically to deal with such an issue. A chief data officer can oversee responsible content use, while strict internal data use rules can guide ongoing governance efforts.

Data silos and integration issues

When information or teams become heavily siloed, the result can be a breakdown in effective data usage for business purposes. Employees who are unable to communicate and collaborate easily can struggle to apply data in a problem-solving context. Technology issues can have a similar effect on companies' effectiveness.

Divisions between departments are problematic because they can lead to incomplete or inconsistent analysis. When teams aren't working with a clear, centralized reservoir of data, the overall strategies coming from analytics can't reach their full potential.

The best way to break down data silos, and to make sure the data moat is shared by the whole organization, is to invest in assistance from third-party experts. These specialized outside contributors can apply best practices to the creation and upkeep of the data moat from both operational and technological perspectives.

The future outlook for data moats

The best practices around the proper use of data are transforming. In years past, analyzing past performance was the primary way to generate insights from information. Today, companies are becoming ever faster with their analysis, focusing on real-time and predictive models.

The changes brought on by the use of AI also go beyond intellectual property and governance issues. Organizations are likely to be very careful about the data sets they use to train their generative models, to ensure the results are on target. The rapid evolution of the AI state of the art means the exact nature of AI-focused data moat design is still an evolving concept.

Being open-minded and flexible around data usage is the optimal approach to handling new trends as they emerge. While companies should have strong policies and best practices shaping their data usage, becoming too inflexible about any of these elements could lead to problems as needs change. This flexibility comes most easily to agile startups, but it's a valuable trait at any size of company.

Make your data a source of value

The first step toward realizing the untapped value of your data and building a valuable data moat in business is assessing your current performance. Are you making the most of your information assets, and if not, what kinds of processes and technologies should you implement to start seeing a powerful return on your investment?

Working with experts who have launched numerous businesses' data moat projects in the past is a quick and effective way to turn your data from an untapped resource into an active source of value. Transcenda's teams integrate smoothly with your personnel, upskilling your people and creating data moat programs that yield real ROI.

Contact us to learn more about your data's potential.

Robert Balaban is a Senior Data Engineer at Transcenda. With a strong focus on data pipeline design, optimization and data-driven decision-making, Robert’s focus is transforming raw information into actionable insights.

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