Your BI reporting roadmap
The specific dynamics of any business intelligence implementation strategy will vary according to BI priorities, technical capabilities, budgets and ambitions. (For greater detail, here’s a full article on BI strategies.) However, any roadmap to successful BI reporting should include the following key steps.
Define your objectives and success criteria
As with most major projects, the first step is to articulate the strategic goals and success measures of the BI implementation. Define the commercial rationale, key deliverables or areas of interest for BI reporting, and the project KPIs with as much specificity as possible.
Be clear about what providing ‘one version of the truth’ could mean in practical terms – from accelerating decision-making to operational efficiencies to revenue gains. Consult a broad cross-section of departmental stakeholders to ensure the targeted improvements (and success criteria) align with overall business goals.
Consider scope and scalability
For some businesses it will be appropriate to leverage every single data source and seek actionable insights for every business function from day one. However, given the time, money and resource implications, some might also consider a more limited introduction of business intelligence tools from which to build a proof of concept, build platform understanding, and build wider executive buy-in. A head-turning, insight-providing BI report pitched to the right audience can serve as a useful catalyst for scaling-up to full deployment.
Audit your data landscape
A two-level audit of your data landscape is crucial to business intelligence reporting success:
- Data quality assessment: Scrutinize every data source under consideration for use in the BI platform. This includes internal business applications, CRM tools, ERP systems – any source of potentially relevant data. Gauge the quality of the data in terms of accuracy, completeness, reliability and accessibility. Only reliable, high quality data should be loaded into the BI solution.
- Data structure assessment: How and where your data is processed will have a material impact on your ultimate choice of BI tool. With some businesses, data remains unstructured and scattered across multiple spreadsheets, applications and databases. Others consolidate their data sets in some fashion – such as via (on a scale of unstructured to highly structured) a data lake, data warehouse or data mart.
You need to pinpoint where your business is on this spectrum, and determine whether the data is on premises, in the cloud or a mixture of both. This detailed data landscape knowledge will ensure you select a BI solution that can access and process your data – and therefore generate BI reports successfully.
Determine data integration processes
Many modern BI solutions provide their own data integration and ETL (Extract, Transform, Load) connectors – aligning, cleansing and consolidating data to flow into the business intelligence platform. However it’s vital to check if your particular data architecture is compatible with specific BI platforms, or whether you will need to carry out in-house data integration first.
Select your BI solution
When looking for a BI platform that’s a good fit for needs, the most important selection criteria include:
- Connectivity and integration: Check how easily the platform can integrate and access your data sets (with as little intervention from you as possible).
- Team technical capabilities: Consider the team that will set-up, roll-out and manage your BI reporting and select a platform offering the right level of support and user-friendliness to match their level of technical expertise.
- Scalability: Whatever your entry point to BI reporting, from pilot program to full deployment, look for a platform with the flexibility and infrastructure to scale to your longer-term needs.
- Reports / visualizations / analytics: Ensure the BI software you select has a wide enough range of customizable reporting capabilities and visualization options to suit your needs – including full mobile BI optimization.
- Artificial intelligence options: Depending on budgets, data preparedness and desired BI sophistication, it is worth investigating how different business intelligence reporting tools incorporate some application of AI. This ranges from accelerating data preparation and data processing, provision of predictive analytics and even NLP (natural language processing) interfaces that allow business users to query data sets using everyday language.
- Cloud-based or on-premises: There are pros and cons to cloud business intelligence platforms. Initial set-up time and costs tend to be lower than on-premises alternatives, and cloud-based BI solutions place particular emphasis on easy to use interfaces. However, the cost differential for on-premises platforms evens up over time, they tend to be more customizable than cloud alternatives and may offer faster processing and querying speeds.
- Self-service options: Levels of self-service flexibility vary between BI solutions, so consider the degree of independent insight discovery you are looking to offer your teams.
- Embedded BI / analytics: Rather than using a separate BI application, there’s a developing trend for dashboards and visualizations that integrate into a user’s existing software. Consider whether this might be a good fit for your stakeholders.
There’s a lot to consider but it’s worth shopping around to find the right fit as there are many BI providers from which to choose, such as: Microsoft (Power BI), Tableau, Qlik, Domo, IBM (Cognos Analytics), AWS QuickSight, and Oracle (Analytics Cloud).
Change management and user engagement
For business intelligence reporting to have maximum impact, broad-based stakeholder buy-in is essential. You need to include representatives from every department involved, and you need to include them early in the process. A new business intelligence report or interactive dashboard will move some colleagues away from familiar processes and applications. Employ a targeted change management program (including provision of training resources) to diffuse any resentment or resistance and to promote the benefits of BI.
Create targeted, intuitive BI reporting
When creating a data visualization, dashboard or report, keep in mind the recipient may not be familiar with BI reporting tools. So make them both visually striking and, crucially, intuitive to understand and interact with. Insights should be self-evident, requiring a minimum of explanation. Ensure the content and layouts are tailored to the different user personas for maximum relevance and seek regular feedback from key stakeholders.
Implement data governance
Combining this much commercially sensitive data in a single platform necessitates the creation of robust governance protocols. These should include:
- Data security: Work with your BI provider and your IT team to create a strict security framework around each stage of the business intelligence lifecycle, from data storage, transfer and ingestion to access and usage monitoring.
- Access, ownership and permissions controls: It might not be appropriate for all business users to have full access, data analysis and sharing rights with all of the data, so set role-appropriate permissions levels and define data ownership.
- Regulatory compliance: Ensure you have clear data stewardship protocols to maintain compliance with relevant industry regulations and data privacy laws.
Monitor and optimize BI reporting
BI reporting should involve a process of continual evolution and improvement, so monitor the performance and impact of your BI system. ‘Performance BI’ tools and techniques allow you to track important platform KPIs, covering:
- Business performance: Track how business intelligence reporting has performed against its original success measures – how far it has helped accelerate the achievement of strategic business goals.
- Data quality: Guard against inaccurate, incomplete or unreliable data to be ingested into the model – undermining the value of all insights and data analytics.
- Infrastructure: Ensure that the BI systems are consistently available and robust enough to provide an optimal service (for example fast query and visual rendering speeds) irrespective of data volumes and user demands.
- User adoption: It’s important to track user adoption and usage metrics to optimize report content, identify potential BI super users and understand evolving training needs.
