Data driven management is 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, analyse and act on data gains momentum.
As such, business teams with deep business process and subject matter understanding can now own a larger part of the creation of data products (datasets, dashboards & algorithms). Technology vendors have acted on this trend by releasing more business friendly exploration, dashboarding and statistical tools. This has a tremendous impact on the process of creating data products in organisations 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. Then 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 in enough detail… and he waits until the next iteration of the product.
Internal people that thoroughly understand the business problem at hand 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 that have access to the data and tools do not understand the business problem well enough to come up with effective solutions.
A paradox that is common in many organisations 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.
When subject matter experts and business analysts are enabled, your data products will become more meaningful. They will have more impact on your business and will be produced faster, cheaper and of higher quality.
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 access the data that they need at all times.
Secondly, have a catalogue of tools for data transformations and visualisations. This selection of tools should fit your use-cases, the skill-level of your people and can evolve over time.
Lastly, provide a secure, governed environment to share datasets, dashboards and algorithms effectively 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. Once they start to produce useful data products, decision makers in the organization will want more…and more..
The time of enabled analysts is extremely valuable. 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, versionize 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 standardising 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 free 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 to 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. It 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. So who can create and maintain this platform?
A Data Analytics Platform team (A DAP-team) can do this. It 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 analyse this data efficiently. The team can be outsourced as a whole, 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 it is important that you 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 awesome data products that matter for your organisation.
We’d strongly recommend using a Public Cloud platform as a foundation.
With Cloud you can automate many of the 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 on the market.
Step 4 – Create an effective delivery process that allows your data team and data platform team to deliver and learn together.
Project management – think prince2 or PMBOK – often restricts the iterative creation process that is typical in data analytics. Rather organise your data practice on product management principles. Define a product owner role and a delivery process for your team. The product owner will help you to effectively communicate the needs of your data team. He 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 making sure your platform is secure and robust. Adapted agile/scrum methodologies and tooling can help to streamline communication between Data teams 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 public cloud. Your Data Team should play to theirs.
We believe organisations should promote data literacy and let the analysts own their dashboards and algorithms. We believe they should invest in a data team and make their business analysts hyper-efficient. Such a team is an asset for the organisation and should be treated as one. Creating a data team will be a journey of change. But when done correctly, you will get results fast.
Tropos can help achieve these results and guide you on your journey, from start to finish.