Building A Team To Put Your Data To Work

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Companies struggle to make use of the large amounts of data they have. Companies usually start big projects to attempt to take care of every data user’s needs, or they have separate groups of people working on different data pipelines that can’t be reused. Firms need to find ways to use data that will help them in the short term and also prepare them for future data use.

Companies that are successful treat data like it is a commercial product. In order to increase sales, businesses will often create a standard product that can be cater to the needs of different types of customers. A data product works similarly. This software provides high-quality data that can be used by people in different parts of an organization to solve various business problems. The software might, for example, provide 360-degree views of customers, of employees, or of a channel.

Companies are most successful when they treat data like it is a product. A firm will typically try and create a commercial product that can address the needs for as many people as possible to make maximum sales. This generally involves creating a product that can be customized to individual user needs. Automakers entice customers to buy their cars by offering a variety of special options that can be added on to the standard models. These options may include leather upholstery, tinted windows, and anti-theft devices. Most digital apps allow users to personalize their dashboards in some way, whether it’s changing the layout, color scheme, or what content is displayed. Some apps also offer different plans with different pricing structures depending on the user’s needs.

If companies regard data as a product, they can decrease the amount of time it requires to utilize it for new purposes by up to 90%.

Over time, companies improve their products by adding new features or introducing brand-new items. This is usually done in reaction to feedback from users, performance evaluations, or changes in the market. All the while firms seek to increase production efficiency. Wherever possible, they reuse existing processes, machinery, and components. Automakers use a common chassis on vastly different cars in order to reuse blocks of code and deliver value quickly.

What Is a Data Product?

A data product is a set of data that is high quality and ready to use. It can be accessed by people across an organization and used for different business challenges. The example given provides a comprehensive view of 360 degrees of what customers do, including their behaviors, demographic information, payment methods, and interactions with customer service. If a company were to collect this data, they would have a much better understanding of their customers as a whole. It might provide an overview of employees or a specific channel, such as a bank’s branches “Digital twins” are a product that enables the replication of real-world assets or processes using data. This could be something as small as a critical piece of machinery or something as large as an entire factory production line.

Data products have a lot of uses and can make a lot of money. At a large national bank, one customer data product has powered nearly 60 use cases—ranging from real-time scoring of credit risk to chatbots that answer customers’ questions—across multiple channels. The customer data product in question is robust and comprehensive, providing the bank with a 360-degree view of each customer. The applications provide an extra $60 million in revenue each year and stop $40 million in losses. As the product is used for more things, it will keep having a bigger impact.

Each data product is designed to cater to the needs of different types of data “consumers.” Much like how a software product is designed to run on different types of computers with different operating systems. Our work suggests that there are typically five types of consumers that are systems, rather than people. The term “consumption archetype” is used to describe what the data is used for. They include:

1. Digital applications.

This task requires data that is cleaned and organized into the correct format, which could be either individual messages in an event stream or a table of records in a data mart. The data needs to be delivered at a particular frequency. An example of this would be a digital app that tracks the location of a vehicle in real-time using GPS or sensor data. An app that is designed to find trends in customer browsing behavior will need to have access to large amounts of web log data that can be accessed on demand from a data mart.

2. Advanced analytics systems.

Data must be cleaned in order for machine learning and AI systems to be able to process it.

3. Reporting systems.

In order to use data for dashboards or regulatory and compliance activities, the data must be of high quality, well-managed, and have clear definitions. The data must also be delivered in an audited form. In most cases, data is delivered in batches, but there is a trend among companies to move toward self-service models with real-time updates.

4. Discovery sandboxes.

This allows for spontaneous, exploratory analysis of a combination of raw and processed data. Both data scientists and data engineers use these methods to explore data and find new potential applications.

5. External data-sharing systems.

There are strict policies and agreements in place about where data is stored and how it is managed and secured. Banks use systems that allow them to share information about fraud with each other. Retailers use systems that allow them to share data with their suppliers in order to improve their supply chains.

Each consumption archetype has different data storage, processing, and delivery technologies, which need to be arranged in a specific pattern. This is essentially a blueprint for how the necessary technologies should fit together. A pattern for a sandbox would typically include technologies for setting up a self-service environment that can be accessed by multiple users throughout the company. An advanced analytics system that uses real-time data feeds could involve technologies for processing large amounts of unstructured data.

How to build your data team

—— Most businesses are hoping to use data to achieve their business goals. This means creating a strong data team that can share its insights effectively with different parts of the company. Unfortunately, this is no easy task.

In order to be data-driven, companies need to have a data strategy, data governance, and data analytics.

An organization’s data strategy is the plan for how data will be used to help achieve its goals. To successfully execute a business strategy, you must have a clear understanding of the data involved. Why are you collecting data? Which of the following are you trying to accomplish: making money, saving money, managing risk, or delivering an exceptional customer experience?

The term “data governance” refers to a set of processes, roles, policies, standards, and metrics that help ensure the efficient use of information within an organization, thereby helping the organization achieve its goals. Data governance is the process of making sure data is accurate and available. A data governance strategy is a plan for making sure this happens.

Data analytics is the process of analyzing data to draw conclusions. The three most common roles in data analytics are data engineers, data analysts, and data scientists.

In the end, you will only be able to use data as effectively as these three foundations allow. If you’re reading this and realizing that your organization does not have any of these, don’t worry. A good strategy for improving your business analytics is to build a strong, dedicated analytics team that is closely aligned with your company’s strategic goals. This article is about the first pillar of data organization, which is the focus of the article.

When building a data analytics team, heads of data typically grapple with the following questions:

It is essential for companies to have a strong data team because data is essential for survival today.

Where are you in your data journey?

Before you can build an effective data team, you need to understand where you are in your organization’s “data journey.” The stage of your data journey will dictate the structure of your data team. This section simplifies the data maturity assessment. Two things to be aware of are company size and data maturity. Although your company may be large, it may not have a lot of data maturity.

The goal of data maturity is to see tangible value from your data assets.

Key players on a data analytics team

A data analytics team typically consists of four key functions: -Data collection -Data processing -Data analysis -Data presentation

  1. Data Engineer: They are responsible for designing, building, and maintaining datasets that can be leveraged in data projects. As such, data engineers closely work with both data scientists and data analysts. We also include the new role of analytics engineer here, although, in practice, this role lies between analytics and engineering.
  2. Data scientists: They use advanced mathematics and statistics, and programming tools to build predictive models. The roles of data scientists and data analysts are pretty similar, but data scientists focus more on predictive analytics than descriptive analytics.
  3. Data analyst: They use data to perform reporting and direct analysis. Whereas data scientists and engineers typically interact with data in its raw or unrefined states, analysts work with data that’s already been cleaned and transformed into more user-friendly formats.
  4. Business analyst/ops analyst: They help the organization improve its processes and systems. They focus on dashboarding, answer business questions and propose their interpretation. They are agile and straddle the line between IT and the business to help bridge the gap and improve efficiency. They frequently work with a specific business area such as marketing or finance, and their SQL literacy can range from basic dashboarding to advanced analysis.
  5. Head of data analytics: They provide strategic oversight to the data team. Their goal is to create an environment that allows all different parties to access the data they need painlessly, build the skills of the business to draw meaningful insights from the data, and ensure data governance. They also act as a bridge between the data team and the main business unit, acting both as a visionary and a technical lead.

How does the data team integrate with the company?

The structure of an analytics team is not set in stone and is subject to change. If your data team’s structure has remained the same for the past two years, it is likely not as effective as it could be. Why? As your company’s data needs are constantly changing, you will need to change your data team’s structure to match. This means that if your organization is not flexible, it will be more difficult to make changes in the future. The reason we don’t prescribe a given structure is that the most common models can be suited to different types of businesses.

The first step to forming your data team is to identify the data professionals who already work within your organization. Data analysts are not the only people who work with data. Business analysts and ops analysts often need to use data in their work, and they may already have SQL skills. If you don’t carefully locate pre-existing data people, you are likely to end up with a data team structure that doesn’t fit your business needs.

Creating a robust analytics team is an essential step you need to take if you want your company to operate using data. How much you will get out of data in terms of business value depends on how strong the team is and how harmoniously it works with the rest of the business. There is no one-size-fits-all advice for the size, composition, and structure of your data team. It is essential to comprehend the data sophistication level of your corporation in order to construct a data squad adapted to your business requirements and consistent with your business strategy.


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