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Implementing technical solutions

Read about the technical skills and systems that are required to address the key challenges of becoming a data-led organization.
Two boxes. The one one on the left side has a teal and pink background with the words

In this activity we’re going to take a closer look at the technical solutions that need to be set up by all organizations who are making the journey towards being data-led.

Technical implementation requires planning and consultation with the business teams. First, we need to understand the business challenges, organizational goals, and resourcing implementations. After that it becomes a question of:

  • Hiring specialist roles or identifying existing employees who can be reskilled or upskilled
  • Choosing the right software and technology solutions
  • Setting up and maintaining systems (pipelines, warehouse, data visualization tools, etc.)

Addressing technical skills (people):

Since the technical skills within data analytics are well-defined, addressing technical challenges is a more straightforward process. People can train over a number of years to learn the skills needed. Acquiring the correct technical skills for the organization often requires hiring for the right skills at the right time.

Addressing technology solutions & systems:

The same applies to technology solutions for data analytics. These are well defined. In fact, new technologies and solutions are designed and implemented all the time. It is a question of understanding the business requirements, developing the data strategy, and deploying the relevant existing technology solutions. Ensuring the organization keeps on top of new solutions will be important as the data analytics implementation progresses. Addressing technology solutions is a constant exercise that is more aligned to resourcing.

Addressing technical skills (people)

A lot of attention in data analytics goes toward the software and technical solutions and innovations that drive the data process. While these are important, it is people who drive data analytics. Our uniquely human skills allow us to make meaning out of data, create insights, and apply them toward the organization’s goals and objectives.

These technical skills have different layers:

  • Upskilling and reskilling non-data specific roles
  • Specialist data hires
  • Specialist consultants to aid the business transformation

Upskilling & reskilling

Just as we no longer ask potential hires if they’re able to use word processing software, we won’t need to ask if people have the skills to interpret data reports or visualizations in a few years’ time. For now, however, it is crucial that non-data teams are upskilled to enable them to work with data and understand how being data-led will transform aspects of their role. Reskilling and upskilling should focus not only on attitudes and knowledge about data analytics, but also on skills for finding and manipulating data at every level, including senior management levels.

Specialist data hires

For the data analytics transformation, some specialist skills will need to be brought into the organization. Roles that require years of training such as data engineers and data scientists are required to build the technical infrastructure, and to extract, transform and load the data.

They use two key methods to discover historical data:

  • Data aggregation
  • Data mining (also known as data discovery)

Data aggregation is the process of collecting and organizing data to create manageable data sets. These data sets are then used in the data mining phase where patterns, trends and meaning are identified and then presented in an understandable way. This allows us to create reports and receive deeper insights more quickly and more accurately as the data is cleaned. This also helps us to take advantage of the drag and drop feature of the data visualization tools because the data is transformed into meaningful metrics understood by every stakeholder. The benefits include access to quicker, more diverse reports and the ability to combine descriptive analytics reports with other data, in order to prepare data analytics reports.

These specialist data hires will be responsible for setting up the data pipelines, data warehousing, cleaning and transforming the data, implementing the initial data security solutions, and setting up data visualization tools.

Specialist consultants to aid the business transformation

Some organizations have internal data champions who start the process of data analytics. Other organizations may wish to hire a data analytics consultant in much the same way that one might hire a change management consultant. The consultant’s job would be to help the organization with the technical aspects of setting up a data analytics program.

Typically, a data analytics consultant would have years of experience with using data analytics to set up data systems, and taking teams from having a non-formal data set up right through to predictive and prescriptive analytics. They’ll have experience being the bridge between business requirements and technical solutions. They’re responsible for the data strategy and data management.

Just as an organization may hire a data analytics consultant to help with the technical challenges, they may also consider hiring a change management consultant to help with the adaptive challenges.

For more information, you can download the following resource at the end of this step: “Difference between a change management consultant and a data management consultant”.

It’s not absolutely necessary for an organization to hire a data analytics consultant or a change management consultant. Whether or not you hire them, next we will take you through the steps you need to consider for setting up the technical aspects of the data analytics system.

Technology solutions

Now that you’ve looked at the adaptive and technical challenges, we’re going to talk you through the practical steps of setting up the data analytics infrastructure.

We’ll begin with the initial steps that businesses need to take and the practical set up that’s required.

Four black boxes in a grid, with the boxes respectively having the words "Data Pipeline", "Data Warehousing", "Data Protection" and "Data Visualization".

These four technical solutions need to be implemented to enable an efficient data flow, which ultimately allows the organization to become data-led:

  • A data pipeline
  • A data warehouse
  • Data protection
  • Data visualization

Over the next few steps, we’ll go through each of these solutions, explaining what they are, what tools are needed to implement them, and why they’re so essential.

Who’s involved in the technical set up decisions?

But first, it’s important to discuss who makes the decisions on how to implement these solutions. Although these are technical challenges, the process requires all departments and stakeholders, and not just the data analytics team and the organization’s leadership.

Designing a data analytics set up and deciding on its features and components is dependent on the business needs and how the data is going to be used. It requires a multi-functional and multi-departmental focus and collaboration.

The first step is to bring together all the departments and stakeholders to define the business needs and to explore how the organization may wish to use their data.

What happens once the business needs have been defined?

The data analytics technical team will work with the business leaders to take the findings of business requirements and make decisions around the set up and maintenance of the organization’s data analytics from a technical point of view. As well as business requirements, the data analytics team and the leadership team will need to make resourcing decisions regarding the set up and maintenance.

Now it’s time to go through the four solutions. We’ll start with the data pipeline and data warehousing…

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A Beginner’s Guide to Data Analytics

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