The complex world of Business Intelligence (BI), data analysis, and Machine Learning can be complex and limitless. With innovations happening at a rapid pace, enterprises can often find it difficult to identify the best strategy and course of action.

Regardless, the benefits of BI can’t be ignored. From at-a-glance insights produced from innovative data visualisation tools to real-time reporting from multiple data sources, BI tools are changing the way that enterprises and teams interact with, value, and access their data.

For these organisations looking to access and take advantage of data-driven strategic insights, they must take care to avoid common challenges and pitfalls – or risk wasting valuable budget and resources.

As experienced BI consultants, we’re here to help. We’ve collected some of our most valuable tips and hints to help your teams get closer to valuable insights, without sacrificing the integrity of their architecture.

These tips include:

1. Identify the objectives and KPIs of projects in advance
2. Understand your long-term roadmap
3. Prioritise data quality
4. Communicate the value of collaborative BI
5. Be mindful of the limitations of your architecture

Tip #1: Identify the objectives and KPIs of projects in advance

As enterprises are made aware of the term Business Intelligence, and realise the benefits it brings, they may wish to quickly implement a project in a short timeframe. However, before enterprises and teams introduce new BI projects or solutions, it’s important to identify and recognise the core objectives of a project and the project scope, as well as how success will be measured.

Without these measurable objectives, a clear rationale behind Business Intelligence projects, or clearly communicating these projects with relevant teams and users, projects may become redundant and unused. What’s more, if teams begin implementation without a clear scope and timeframe, the drain on both resource and budget may have a greater effect on a business than anticipated.

You can begin outlining the objectives, KPIs, and scope of a project by asking questions such as:

• What does this project hope to achieve?
• What types of data are required, and how will we integrate data sources?
• Do we have a data management process to secure reliable results?
• Is the data for this project cloud-based or on-premise, and how will we accommodate this?

Tip #2: Understand your long-term roadmap

As well as identifying the objective and KPIs of projects before you begin, it’s important to approach BI with a long-term view in mind.

While enterprises may have an immediate significant issue that they wish to solve using Business Intelligence tools, a poorly planned short-term solution may jeopardise the ability to efficiently handle future goals.

The priorities and architecture of an enterprise will naturally shift and change over time. Building a data strategy before you start can help create short-term implementations that will align with long-term infrastructure changes. With a streamlined approach in mind, any BI platforms or implementations required in the short-term can avoid unnecessary complications later on.

Learn more about the importance of a data strategy, as well as how to get started here.

Tip #3: Prioritise data quality

Poor quality of data in only leads to poor quality of results out.

To ensure that any results you achieve are reliable and actionable, your data needs to be high-quality. This means that data is maintained, securely stored, accurate, and relevant.

Without these factors, results from your BI solution such as a Power BI reporting dashboard will be ineffective and, worst of all, unreliable. We’re truly passionate about trusted intelligence here at DataShapa, and for good reason.

If users feel like they are unable to trust in the results that reports provide, the value of data-driven insights will be lessened in favour of a ‘trust-your-gut’ approach.

This can seriously damage the value of BI projects both now, and in the future – with stakeholders less likely to approve implementations that may actually introduce greater improvements.

Tip #4: Communicate the value of collaborative BI

One of the greatest advantages of Business Intelligence projects is that it allows multiple teams access to centralised, intuitive intelligence.

The greater the ability for your team to collaborate and share intelligence, the more reliable and accurate results and processes are enterprise-wide.

With all users accessing the same reports and centralised data, harmful silos that disrupt and challenge operations can be dissolved. Take the time to communicate the value of collaborative BI solutions, ensuring you highlight the benefits of streamlined data access over traditional data solutions.

Tip #5: Be mindful of the limitations of your architecture

The Business Intelligence landscape is full of advanced analytics solutions that hold the potential to equip users with groundbreaking insight. The predictive and prescriptive capabilities of Machine Learning models allow users to predict future outcomes, and get suggestions on how best to optimise processes. But is this achievable for your enterprise?

Advanced analytics solutions such as ML tools require significant infrastructure, as well as the time and resource to train models to be accurate and tailored to the prescribed task. For enterprises new to Business Intelligence, it is unrealistic to expect to reach these insights without significant investment and prior projects.

Teams should be mindful of the limitations of their architecture – understanding instead how to optimise their intelligence without sacrificing integrity or stability.

For enterprises still seeking Machine Learning analytics, why not get in touch with us for a free initial consultation?

Our Business Intelligence consultancy services

As an experienced BI consultancy, our team of specialists are fully equipped to help bring businesses from a range of industries closer to trusted data-driven intelligence.

To learn more about how we’ve previously helped enable our clients to reach greater data-driven insights, read our customer success stories here.