Effective BI implementation enables business to accelerate through the innovation loop of experimentation, measurement and learning, and keep pace with technological change.
70 percent of U.S. CEOs believe the next three years will be more critical for their industries, than the last 50 years combined. The same CEOs said data analytics is a top-three investment priority over the next three years.But while BI is gaining traction, many businesses still fail to implement it successfully. So where does it go wrong? FluidIT sets out seven questions you need to ask:
1. Are all our stakeholders bought in?
BI is a tool for business not a pet project for a core group. Every successful BI initiative starts with the education of key users at all levels of the business so they understand how they can use it to be more effective. Without this, your BI implementation will be under-valued, under-used and ultimately, it won’t deliver.
2. Am I asking the right questions aligned to my business strategy?
If you’re not asking the right questions, you won't get the right answers. Get senior leaders on board to define the key performance indicators (KPIs) and ensure they are aligned to your business strategy. A retailer might ask, ‘How many sales did product X get?’ But when you ALSO know the shops’ product placements, promotions, customer demographics and even the weather patterns, and understand how they affect sales your strategic decision-making can be radically improved.
3. Are we capturing all the data electronically?
Often the things you want to measure at a strategic level, aren't always captured electronically at source. This means inefficiencies in manually capturing data, increased risk of errors, and lags in information. If a business wants to know how store configuration affects sales, it needs a digital record of each store’s configuration and your BI system can then cross-reference this with sales and produce a report. Mapping out where your data is paper based or manually captured can identify areas where digital apps could enhance data insights and efficiency and avoid missing key insights.
4. Is the data of adequate quality?
Good quality data is standardised data. If your data is inconsistent, the answers you get will be inaccurate. Imagine if a business wanted to know why they were getting higher recalls than normal on a new product. If different quality controllers are recording the same issue as ‘faulty part Y’ and as ‘product defect Z’, then this variation will make it impossible to get the right answer out of the system. Putting in system controls to reduce this likelihood make for more accurate data insights.
5. Is your data integrated, aligned and stored and accessed centrally?
If your business’s information is captured in disparate systems then you’ll get disparate answers which take time to manually collate and analyse. By feeding separate data sets into a single data warehouse or BI tool, the power of your data analytics and BI insights will be significantly enhanced.
6. Have you adopted the right BI and data analytics tools?
One of the most powerful motivations for investing in a BI tool is the 'self serve; ability it gives everyone to automatically generate real time reports and portray results in meaningful visualisations. The right tools reduce the technical know how needed by end users, reducing the reliance on technical developers to build and share new reports, and integrate new data sources. This puts more power in the hands of business users, and reduces the dependency on central IT to deliver.
7. Is the overall BI initiative likely to be cost effective?
BI initiatives can easily become the tail that wags the dog. Adopt agile development approaches to ensure that you rapidly deliver dashboards and data insights that generate business value, rather than trying shove everything into a data warehouse and then look to get value out of it later. Start small and incrementally rather than trying to design an all encompassing solution that meets every single business requirement. With an incremental approach you can stop when you've spent enough or where the value has diminuished, whereas with a waterfall approach, you may be stuck on an expensive train that you can't get off.