Far too often driving analytics in companies comes down to single analytics rehearsals conducted by just a few enlightened individuals who have noticed the power of data. Today, the majority of analytics development projects are conducted in corners and silos, without sharing insights between business units. This is a good start, but it is not enough. The true benefits of driving analytics remain unattainable.
It is generally acknowledged that data is one of an organization’s most valuable assets. By utilizing data and analytics, companies can deepen their understanding about their products and customer behavior. Turning data into insight makes producing high-quality products possible and increases customer satisfaction.
What is often missed, is the fact that the analytics rehearsals are not one-time operations that deliver value immediately after the first release date. Getting the most out of data and analytics calls for a comprehensive strategy to push every function in the company towards a shared goal. It also requires an indomitable digital product and service development effort to capture data, nurture it and launch desirable products and services that increase customer engagement.
Companies also need to aim higher. Being able to predict upcoming phenomena, foster automation development and, therefore, increase productivity of the various processes is the key to getting more out of traditional products and services. This doesn’t happen without driving company-wide transformation programs that share a clear strategic business vision. Analytics capabilities that solve business problems must be delivered across different lines of business.
First, it is important to identify the most promising initiatives to obtain the assumed payback for the investment. Driving development on something that doesn’t provide enough benefits, is a waste of time and effort.
“Conduct feasibility assessments to reveal the potential of each use case”
There are always more ideas than pairs of hands for the job. Nevertheless, it is worthwhile to conduct feasibility assessments that reveal the true potential of each use case. That is the way to avoid initiating development activities that don’t offer sufficient payback for the investment. Sometimes initiating proof-of-concepts is the best way to present concrete results. Spending a few weeks testing and validating concepts might save months in development time, when further development initiatives have been initiated upon proven technologies and tested concepts. The best use cases are the ones that comprise a great business opportunity, an appropriate time scale and low complexity. If the use case falls into the category “nice-to-have” and “low business benefits”, it is time to walk away. If the complexity level is too high compared to the obtained benefits, the use case needs to stay untouched. Assessing and selecting the best use cases based on business strategy:
Increases customer satisfaction
Improves the quality of the product or service
Reduces waste in development
Improves product differentiation
Moving from subjective to objective decision making happens when the decisions are based more on collected data. If the use cases were selected earlier based on enlightened guesses, today it is crucial to collect performance data throughout the development period to support decision-making. Therefore, KPIs must be available soon after the initiation of the analytics implementation. If KPI’s show poor performance or low ROI, the initiative needs to be terminated. It might sound harsh but it is definitely the right thing to do.
Making decisions based on data makes sense to everyone. If the case appears to be a dead-end according to KPI’s, there will be fewer people that want to bang their heads against a wall. Changing the course will be easier, when the decisions are based on data, not intuition.
Making sure that the KPI’s are measuring the right phenomenon is still crucial. It is true that you get what you measure. Validating KPI’s needs to be conducted on timely basis to maintain confidence in the selected direction. The right KPI’s help to validate if you are on the right track with your analytics development activities and if you have made the right decisions in the past.
“Set correct KPIs, follow the metrics, act accordingly”
Correct KPI’s measure the success of analytics deployment outcomes. Topical questions are:
Have the new analytics-boosted products provided new revenue?
Has a targeted process resulted in improvements, as assumed?
Have we reached the target to reduce costs, while operating products that utilize new analytics algorithms?
As mentioned above, the reality is that analytics development should be prioritized in a deliberate way, based on business priorities and overall corporate strategy. What is also noticeable, is that the strategic objectives are just targets and guidelines for development. Strategies do not turn into data-based services by themselves. Sufficient resources, such as budget, tools and human talent are required to turn strategic objectives into tangible outcomes.
It is also crucial to ensure that all the relevant enablers are available. Analytics development usually requires high quality data. However, what is the situation in your company? Is your data governance in good shape? How about the integrations to business systems?
Focusing all the power into enabler development will slow down the actual analytics development. This is not the way to reach the target. There is always technical debt, missing features and things to fix. Finding the right balance between supportive development and analytics initiatives will lead to the best outcome.
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