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12 steps to a successful business intelligence strategy
A robust business intelligence (BI) strategy is essential for organizations to navigate today’s rapidly changing commercial landscape. Leveraging colossal volumes of business data to enhance decision-making and unlock valuable insights, BI can accelerate operational optimization, drive business growth and generate competitive advantage.
Whether you’re a small startup or a large enterprise, creating and implementing an effective BI strategy requires a lot of careful planning, technical skill, change management and coordinated cooperation between teams. Because the BI road to actionable insights isn’t a question of IT plugging in a new piece of tech, it’s an ongoing business-wide commitment.
Read on to explore the key steps you need to take when creating a business intelligence strategy – from planning and pre-delivery to implementation and iterative improvements.
Step 1: Define your BI objectives
Any BI roadmap must have a destination in mind. In order to design, resource and deliver a BI implementation to greatest commercial advantage, it’s essential that your strategy starts with a clear vision of business intelligence objectives. Define with as much specificity as possible the benefits you want to generate with BI (such as better business decisions, improved efficiency or enhanced customer experience).
Naturally these objectives should align with and support overall business goals or targets. Where possible establish quantifiable success measures and KPIs for the BI deployment.
Step 2: Seek executive sponsorship
Successfully implementing an organization-wide BI solution requires winning the hearts and minds of colleagues from every function. But this needs to start with sponsorship and support from business leadership for several reasons. Firstly, embedding business intelligence requires a commitment of time, money and people (and these commitments must be signed-off). Secondly, gaining executive support also provides the chance to verify the alignment between business goals and BI objectives.
And lastly, projects that have the backing of the bosses tend to generate sufficient momentum to overcome any institutional resistance to change. Securing executive support will typically rely on a clear communication of a positive cost / benefit analysis and timeline for the BI deployment.
Step 3: Broaden stakeholder involvement, build your BI team
Regardless of the specific BI platform, successful business intelligence strategies live and die according to their capacity to engage business-wide stakeholders. Initial stakeholder conversations need to set out the BI vision, seek input from each department to gauge their business intelligence needs, and begin discussions around resourcing requirements and change management initiatives.
For many (most) businesses, building a BI team won’t be about recruiting legions of new business intelligence analysts and IT professionals. Instead it will be about assigning BI responsibilities to existing team members (and providing the training where necessary) for successful project delivery and ongoing BI management. This might include liaising with BI software providers, oversight of data and platform integration, and BI reporting.
Step 4: Settle on scope
Like any new project, implementing a successful BI strategy needs to return positive results as quickly as possible – so think carefully about the scope of your initial implementation (which functions, processes, or data variables to incorporate). Working with your key stakeholders, consider a pilot BI project to iron out any data, process, personnel or infrastructure issues and to demonstrate value. This will prepare the ground for a more extensive business rollout.
Step 5: Define your initial BI Roadmap
Putting together a business intelligence roadmap is important to a successful BI strategy. It provides the framework and key milestones for BI implementation. It’s also a useful gauge for business leadership to track not just progress toward value realization, but also their part in ensuring key dependencies are met.
Step 6: Select your BI data
Clearly defined business intelligence objectives and close consultation with key stakeholders should help map which data sets – both internal and external – would add value to the BI model and should therefore be included in the data integration. The balance to be struck here is consolidating sufficient data to draw business-wide insights, without including more peripheral information ‘just in case it’s useful’. The more disparate data sets are included, the more complex the alignment and rationalization process will be – potentially slowing BI’s speed to value.
Data management at this stage is not simply a matter of volume, assessing data quality is crucial. Incorporating inconsistent or incomplete information of questionable accuracy pollutes the BI solution and can distort data analysis. Restrict your BI journey to high quality, highly relevant data. If there are gaps in, or quality concerns about, data sets you wish to include in BI reporting, it could be more prudent to invest the time in bringing this information up to standard before including it in business intelligence analysis.
Step 7: Choosing the right BI solution
There are many BI solutions from which to choose, with varying features and capabilities. Compare vendor offerings before committing to anything and, where possible, look for a detailed service level agreement. Careful due diligence during the selection process will help maximize ROI and provide the insights you require. Most BI software will include a suite of data mining, dashboards, data visualization, BI reporting and alerts (again, of varying sophistication). However considerations when selecting your BI platform should include:
Infrastructure fit: BI tool capabilities vary and organizations’ data architectures vary – and they’re not always a good fit. Check that your BI solution can integrate with your IT infrastructure, access your data sets and draw out the relevant information.
Technical capability: Consider how much support your business is likely to need from a provider in the set-up and ongoing data management of your business intelligence solution.
User experience: A BI platform needs to be user friendly if it is going to be useful and used. Examine how easily non-technical stakeholders (not just business intelligence analysts) will be able to interact with and query the BI platform.
Scalability: Your BI solution needs to scale according to your needs – starting at a level and cost appropriate to initial use cases and capable of ramping up as the demand for BI insights grows.
Data analytics and drilldowns: Ensure your BI software provides the interactivity you require to drill down into your data.
Sophistication and outputs: Be clear on what you need from your platform in terms of outputs. For example some BI solutions now (inevitably) offer artificial intelligence and machine learning to power predictive analytics – forecasting what is likely to happen next based upon historical data. Such systems can take longer to integrate (incorporating business rules) and may come at a higher cost, but for many businesses the strategic outputs justify both the time and money.
Step 8: Data governance
Business intelligence solutions democratize business insights. They broaden the cross section of business users able to access and understand an organization’s performance data. But BI governance safeguards should be put in place to ensure insight is shared without compromising data security or privacy. Data permissions protocols are fairly standard in modern BI applications – enabling organizations to specify who is able to access which data insights. However, such out-of-the-box options need to be supplemented with clear data governance processes and monitoring.
Step 9: Proactive change management
A successful business intelligence strategy must include a change management component. In principle, a BI platform is a hugely valuable data source that most business users would want to use. In practice, business-wide buy-in and platform usage requires proactive and ongoing change management initiatives. These should include:
Clear communications on the rationale for introducing BI, how it will be used by the business and how it should be used by individuals
Transparency on how business intelligence will intersect with peoples’ daily routines
Demonstration of benefits to individuals
Training on BI software best practices, how to perform common tasks, and where to channel help requests
Monitoring BI platform usage and compliance – this includes seeking regular user feedback and provision of retraining materials
A comprehensive change management program maximizes the adoption, and therefore impact, of business intelligence solutions.
Step 10: Data literacy, data advocacy
Business intelligence delivers greatest ongoing value when it becomes simply the tip of the spear in a data-informed, data-literate organization. Rather than thinking in terms of an individual application or BI initiative, a successful business intelligence strategy will work to instill a company culture that values and advocates data driven decisions. In such a culture, businesses invest in BI infrastructure and data architecture, and provide all the training and encouragement employees need to generate maximum value from BI solutions.
Step 11: Monitor, optimize, iterate
Regularly monitoring and optimizing your BI solution’s performance is crucial. Continuously analyzing and adjusting both processes and technology enables your BI function to evolve and your business to improve data quality, reporting accuracy, and overall effectiveness. This iterative approach ensures that the BI strategy remains aligned with the organization's changing needs and goals, while leveraging the latest advancements in data analytics.
Step 12: Activate your insights
Generating insights is one thing, acting on them is quite another. To maximize the value-add, a business intelligence strategy needs to include a view of how insights can be translated into action. Here, however, standard BI reporting can hit a stumbling block. While revealing significant trends and key performance indicators undoubtedly informs decision-making, it doesn’t always explain the underlying causes or how to address any underperformance.
This is where including process mining insights from Process Intelligence into your overall BI strategy can be transformative. Process Intelligence provides a living, breathing digital twin of an organization’s processes and how they interact with each other across entire workflows. This enables value opportunities to be uncovered and process anomalies to be identified and addressed. BI insights become actionable in near real time. What’s more, Process Intelligence also layers in standardized process knowledge and best practices, providing optimal approaches to process improvement initiatives.
Read how luxury retailer Globusand life sciences organization Vetter accelerated business intelligence speed to value with Celonis Process Intelligence.
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