Build better dashboards faster and at scale

Data-driven management has been one of the most important trends in business management over the past years. As teams become more and more data literate, the trend to decentralize the means to explore, analyze and act on data gains momentum.

As such, business teams with deep business process and subject matter understanding can now own a more significant part of creating data products (datasets, dashboards & algorithms). Technology vendors have acted on this trend by releasing more business-friendly exploration, dashboarding and statistical tools. He has had a tremendous impact on creating data products in organizations large and small.

It takes time for a consultant to understand how an organisation works.

A typical Business Intelligence project involves subject matter experts that explain their needs to external consultants. They watch from the sidelines until a dataset, dashboard, or algorithm is produced. The subject matter expert provides feedback on details that have been misunderstood or not described sufficiently, and he waits until the next iteration of the product.

Internal people who thoroughly understand the business problem are excluded from solving it. They are not allowed to explore options and opportunities. They are expected to clearly state their intentions without the ability to learn from data. The people who access the data and tools do not understand the business problem well enough to develop effective solutions.

A paradox that is common in many organizations that are growing their data analytics practice. 


Enable business analysts

We believe it is easier to teach people tools and techniques than to teach them the ins and outs of an organization and industry. 

Your data products will become more meaningful when subject matter experts and business analysts are enabled. They will impact your business and be produced faster, cheaper, and higher quality.

Fast iteration is key to learning. Our analysts are learning, so they should be able to iterate fast.


By enabling your people, you: 

  • Speed up time-to-market of your data products
  • Create more effective data products ( You finally get that BI ROI you were promised in the nineties!)
  • Build and keep the insights in-house 
  • Discover opportunities to improve your organisation
  • Make it easier for decision makers to trust insights created by the same people they have relied on for years

First, people should be able to access the data that they need at all times.
Secondly, have a catalog of tools for data transformations and visualizations. His selection of tools should fit your use-cases the skill-level of your people and can evolve.
Lastly, provide a secure, governed environment to effectively share datasets, dashboards, and algorithms with internal or external stakeholders.

Free up time

BI cases are solved faster and more effectively by analysts or data scientists who know your organization. Now they start to produce useful data products, decision-makers in the organization will want more and more.

Let enabled analysts focus on what they love and excel in.

The time of enabled analysts is precious. Don’t let them waste their time. Do not let them maintain existing datasets, dashboards, or algorithms.
Let them work on new designs, test assumptions, and explore opportunities.

Free up analyst time. Identify recurring tasks and automate them. Standardize, version, and monitor data pipelines, streamline data models and manage dependencies automatically, streamline communication, automate data model documentation and user management.

The above tasks are time-consuming but generic.
However, standardizing and automating these tasks requires a different skill-set than analysts and data-scientists typically have. If you want to keep your analysts focused on valuable work, you need to hire a team that can enable them and frees up their time.

Instead of outsourcing the entire creation process of dashboards and algorithms,  we think you should involve your business analysts and help them become more self-reliant and hyper-efficient.

Create a Data Team

A Data Team is a team of enabled analysts that can solve business problems and facilitate smarter decisions with data. This is part of the organization and creates trusted, scalable data products that matter to the organization. They are the ones that can create dashboards and algorithms effectively if they can work on a great Data Analytics Platform.

A Data Analytics Platform is an integrated set of software products and services that:

  • stores and processes data
  • lets analysts transform and visualize data
  • lets analysts share data with colleagues, stakeholders and other software services
  • governs the game rules of working with data

With a great Data Analytics Platform, analysts can solve those cases fast and at a fraction of the cost. Who can create and maintain this platform?

A Data Analytics Platform team (A DAP-team) can do this. This consists of data engineers and cloud engineers that build and maintain a robust, secure cloud-native analytics platform. The team offers sets of trusted data to the Data Team and makes sure they have the means to analyze this data efficiently. The group can be outsourced because their expertise is not tied to the organization.

The Data and DAP-team work together closely.
The DAP team is responsible for enabling your analysts and keeping them productive. The data team will need to communicate clearly what they need to perform their analysis tasks. Therefore you must have a good delivery process in place that:

  • minimizes overhead for both teams
  • makes your data team productive from day 1
  • allows the DAP team to create a compliant, robust and effective platform
  • makes sure that platform features can be delivered with increasing speed

Where do I start?

Step 1 – Start by creating a roadmap for your analytics platform. 

Use our Data Platform Strategy Canvas to guide you towards creating a roadmap.

Step 2 – Grow use-cases using a think-build-run model. 

Step 3 – Build a great Data Analytics Platform.

A great Data Analytics Platform leverages your existing business analysts to create those excellent data products that matter for your organization.

We’d strongly recommend using a Public Cloud platform as a foundation.
With Cloud, you can automate many tasks that enable analytics infrastructure. You can iterate quickly over analytics tools and use them with your data in search of the right fit. Also, you can benefit from governance best practices and efforts available.

Step 4 – Create an effective delivery process that allows your data and platform teams to deliver and learn together.

Project management – think prince2 or PMBOK – often restricts the iterative creation process typical in data analytics. Afterward, organize your data practice on product management principles: acceptable a product owner role and a delivery process for your team. T e product owner will help you effectively communicate your data team’s needs. H will align with a solution architect and the DAP team to build your Data Analytics Platform in 2-week increments.

Every other week you should release features that enable your team or free up their time while ensuring your platform is secure and robust. Adapting agile/scrum methodologies and tooling can help to streamline communication between data and DAP teams.


You understand your business. We understand cloud-first analytics. 

Your DAP team should play to their strengths. They should understand data engineering and the public Cloud. Your Data Team should play to theirs.

We believe organizations should promote data literacy and let the analysts own their dashboards and algorithms. W think they should invest in a data team and make their business analysts hyper-efficient. Such a team is an asset for the organization and should be treated as one. C eating a data team will be a journey of change. B t when done correctly, you will get results fast.
Tropos can help achieve these results and guide you on your journey, from start to finish.

Jo Swenters
Jo Swenters

Related articles