How do you align BI data models with business goals?

One approach to help business stakeholders get the ball rolling on requirements is to have them start "at the end" and work backwards... look at existing line of business reports and artifacts and map each element to the current source.

Aligning business intelligence (BI) data models with business goals is crucial for ensuring that the insights derived from the data are relevant, actionable, and contribute to achieving organizational objectives. Here's a step-by-step approach: Understand Business Goals and Objectives Identify Key Performance Indicators (KPIs) Data Requirements Gathering Design Data Models Data Integration and ETL Processes Visualization and Reporting Continuous Monitoring and Optimization Feedback Loop Alignment with Decision-Making Processes Regular Review and Alignment

This is the million dollar question! If there were a clear and unambiguous answer, many of us would be working on something else. Joking aside, the only approach that has worked for me is to try to reduce the communication gap as much as possible between the IT teams and the business teams. Working to get the technical people to speak and understand the business jargon, and the business people to go up in technical skills. Once this process of acculturation has begun, the trick is to repeat it over and over again. The smaller this gap, the better the data products we create, and in this particular case, the better the data models will answer business questions.

To align your BI data models with your business goals, follow these steps: - Define your business goals and objectives. What are the questions, metrics, indicators, outcomes, and benefits of your data analysis? - Identify your data sources and requirements. Where does your data come from and what data do you need? How can you ensure your data is accurate and consistent? - Develop a logical schema for your data model. How will you organize and structure your data in entities, attributes, and relationships? - Create a physical data model in Power BI or other BI tools. How will you implement your data model with tables, columns, and relationships? How will you define measures, calculations, hierarchies, and aggregations?

Aligning business intelligence (BI) data models with business goals is crucial for ensuring that BI initiatives deliver value and drive strategic decision-making. Here's a structured approach to achieve alignment: Understand Business Goals Identify Key Metrics and KPIs Data Assessment Data Modeling Data Integration Dashboard and Report Development Iterative Refinement Measure Impact By following these steps, organizations can ensure that their BI data models are closely aligned with business goals, enabling them to leverage data effectively to drive success.

In an ecosystem where needs are changing faster and faster, I would choose the approach that allows me the most slack in terms of data granularity and availability, at the lowest cost per change. Storage is getting cheaper, and processing more powerful: in my opinion, modern data architectures need to be able to deal with the unknown and the unexpected without excessive risk. Flexible by definition, this paradigm allows us to create BI data models simply, quickly and efficiently. We can also evolve existing data models, adapting or mutating them to new business requirements.

Previous to model, one needs to think about data architecture as a BI systems often does not stand alone. In an organization, it's always good to have a staging layer where all data is dumped in whichever format the golden source is having them, then an enterprise layer (common semantics for all the organization) and then a consumer layer, with data organized in the way that is optimal for analysis. That enterprise layer should also be able to expose services (Data as a Service) for a more transactional use, have a catalog available (Data Governance). This is the way to ensure we're covering the whole data cycle.

Before choosing a data modelling approach, it is important to understand the Data architecture and key principles that set the guide rails for the data model. My preference is to start with a Logical data model which helps create a common understanding across business and IT. LDM can then be engineered to the most relevant physical data model implementation.

Important, in my experience, is that before you get technical you need to create "User stories". This ensures value creation and aligns the model with Business needs. It is an extremely difficult and often neglected discipline. Asking the difficult questions is a must and often you have to guide and spar to find the "nuggets". Model creation is a collaboration and not a strictly technical exercise.

The right approach is one that combines various data modelling techniques, to inform technologists and non-technologists alike. Data modelling is one of the the earliest opportunities to create data literacy, by demystifying how data feeds business data outputs.

Every business is different, so the best practice approach is the one that tells people what they need to know to build the BI solution, as well as what they need to know to understand it and use it in ways that benefit the business.

The data model should accommodate for further sources in the future giving further insights to the defined KPIs . Another consideration that is on the core of this modeling is data movement . Do we really need to move data into a centralized data store or a virtualization model could be imposed thus saving heaps on ELT | ETL

On naming convention, we should point to the most natural language possible, a name with is easy to understand for the business as most BI tools nowadays offer self-service capabilities. The business needs to be able to understand the data model or view they're using. One other consideration is regarding calculated measures, which often makes analysis more efficient. Having a calculated measure already in the model or only calculated in one dashboard, for instance, will depend on how particular or often that measure is used for analysis.

Here is the thing: This makes sense for heavily centralised enterprises. The more decentralised and the more you think of data meshes, the less it makes sense to enforce such strict guard rails. While letting go of long loved traditions can be painful, the solution lies in data governance methods and tools such as the mentioned data dictionaries, catalogues and metadata repositories. Those can help people understand data and to make use of them, while keeping them under control. Especially in regulated industries this is of paramount importance.

Two best practices to consider are Scalability and Maintainability. A good model should be able to scale upto new requirements. A good data model should also be cost effective to maintain if there are changes needed. Often times, a data model is created which is good for that point in time, however fails the tests of Scalability and Maintainability. A good way to test if the model will hold is to simulate future business changes and see how adaptable the model is.

A key must to testing and validation is having someone in the business that knows and understands the actual raw data and systems to validate accuracy at the root model before aggregation is done. Then again at the aggregation. Too much can happen in between and trust can be lost if this doesn't happen.

This starts with setting data quality standards, as that is the target for data quality testing. A target that doesn't exist, is impossible to hit!

Data volume testing is specially important as often test is done with a small set of data and data model shows good performance. When this model is then used with real volumes, performance decreases dramatically.

Monitoring and updating the data model ensures that it remains relevant and accurate, which in turn leads to better decision-making and more successful outcomes. It is always important to ask feedback from stakeholders to understand their actual needs, improve the data model, build stronger relationships, and ultimately achieve greater satisfaction.

Aligning data models with business goals is essential for extracting value from the data -driven initiatives. Hence continous improvement of the data models is needed as business goals tend to change due to both internal and external factors. This is achieved by establishing feed back loops with business stakeholders and report users as well as continuous monitoring of the data models. Regular assessments help keep models aligned with evolving business needs.

The key here is to embed data governance in other established business processes. A good example is PMO. Add data governance steps to PMO processes, to ensure the right questions and actions are applied to data as a mater of course in projects and programmes.

I'd add that not only the data model needs to be monitored and updated. Also the dashboards should be monitored and analysed. Are we using all the KPIs? Are we missing some? Perhaps the user is downloading the data to a spreadhseet and then doing some analysis there that was missed?

Data is evolving every day - it is crucial to understand what other data (external / internal) when combined can provide additional insights. Building data solutions that can acquire internal and external data while maintaining compliance, security and privacy is essential for data and insights maturity. Establishing a data repository that can scale and integrate with newer sources and visualization capabilities can be a true differentiator.

Data models are there to simplify the consumption of data and get meaningful insights and to take appropriate actions. Best way to align data models ,with known or even not so known business goals , is by doing a top down analysis and to learn 7w’s of the organization. Then perform bottoms up analysis by each data source . Once these two are done , create dimensions data model and create facts and dynamic measures those align by eg OKR and KPI of an organization.

In definining business requirements dig deep and ask why. I've seen countless data models that are structured perfectly giving the business what they wanted, but afterwards finding out that it isn't what they need to solve the problems. This stems from most business areas not knowing what is possible with BI tools and IT in general assuming that they do. Asking questions like why that KPI, who is the user and what persepective do they need to look at the data, what problems are they trying to solve or conditions they are monitoring is paramount to a data model being not just right but successful for the business.

We need to understand the business needs and what type of insights we would like to derive out of it. Understand the audience better who will be using these BI layer. Designing the data model with least granularity is the key for any BI model to be more robust for future use cases as well. Try to bring in only the KPI's into the BI data model to have it more optimised. We also need to make sure complex derivatives and aggregations are done at data Mart or data warehousing, so that it can reduce the load on reports.

Data models should exist as representations of the real world and not in a vacuum. As such, it is useful for any data professional to understand how a business makes its money or how organisations deliver their services to the public, what are the cost drivers and processes, how do different parts of a business define things. Data models need to reflect how an organisation thinks and synthesise different viewpoints and definitions into something different parts of an organisation can relate to.