There is a lot of information about benefits of Data Driven Enterprise. With the most prominent characteristics like following:
- Leverage data to prove multiple theories and choose the best one
- Continuously research business data to find new opportunities
- Seamless integration of ML into business processes to gain extra revenue
- Innovative data techniques resolve challenges in hours, days or weeks
While for some people mentioned benefits might look quite obvious, still understanding how they are made possible might make even stronger mind shift towards data driven culture.
So in this article i would like to explain anthology of data driven approach and how exactly it makes possible to boost enterprise performance so much.
Enterprise effectiveness breakdown
Today any modern enterprise consist of multiple departments. Each department has variety of business processes to follow according to their goals. From organisational perspective department consists of multiple teams and employees.
To make entire enterprise work more efficient, organisations declare tremendous number of rules and policies (aka business processes) which could be generalised as following:
- Enterprise is effective when all of its departments are following their business processes properly
- Enterprise is effective when departments are able to collaborate effectively
- Enterprise is effective when it is able to deliver competitive product to the end consumer
Note: in this schema product quality is derivative of processes and collaboration.
Based on this definition we can highlight 3 core KPIs:
- Effectiveness of business processes
- Effectiveness of cross-team collaboration
- Effectiveness of product delivery
Enterprise data maturity stages
The maturity of data processing practices significantly varies from company to company. Some companies might not be aware of data value at all, while others could have immense experience growing data culture through their employees.
Based on level of data practices penetration into organisation processes, i would like to highlight 3 well distinguished stages.
Note: please remember that there is no clear separation between these 3 stages. In fact continuous improvement of data practices leads from one stage to another. The ultimate goal of Data Driven Enterprise is not to apply strict rules, but instead to follow the right direction by practicing data culture. Where decisions and actions are made based on real use cases for each specific enterprise.
So here are 3 the most prominent stages of data maturity practices within organisation. We will review each stage separately and see how it affects enterprise effectiveness KPIs.
- Stage 1 - Automation
- Stage 2 - Data Awareness
- Stage 3 - Data Driven
Stage 1 - Automation
It is a starting point where company applies initial efforts for automation and/or digitalisation of their business. Particularly it could address following scenarios: standardise specific business case, automate manual workflow, decrease human factor, etc. What is important to understand here is that the goal of Automation is to address one specific business problem in the most straightforward way possible.
At this stage organisation's departments do have a data storages represented as data silos focusing on a single service and strongly isolated from other systems even in scope of the parent department.
Typical achievements of Automation in scope of the core KPIs:
|Processes||Minimising error rate and limiting the role of human factor by automating business processes|
|Collaboration||Decreasing collaboration gaps through well defined workflows|
|Product||Improving predictability of delivery process by mitigating the most common risks addressed by previous 2 KPIs|
Stage 2 - Data Awareness
The next stage after Automation is the Data Awareness. This stage is characterised by initial efforts to separate data from business processes into clean and reusable form. It is often implemented as date warehouse or some other kind of centralised data storage.
This stage helps to implement some quantitative improvements of business processes by reusing existing data sets. Having centralised data storages also helps to cut the costs and maintenance efforts.
Note: at this point enterprise decision makers are responsible to define business processes, which being executed properly by IT department should deliver business results. It’s important to note that first 2 stages Automation and Data Awareness are not intended to change existing organisation processes but rather to improve and optimise them.
Nevertheless this stage is characterised by clear understanding of data value and it’s ability to make an impact. It is a moment when first data leaders arise with idea of increasing product quality improvement turnaround. There are first signs that significant product improvements could be done on the team level.
Data Awareness impact to the core KPIs:
|Processes||Improved time to market|
|Collaboration||Faster collaboration through data sharing|
|Product||Quantitive improvement based on the sum of the previous two KPIs|
Stage 3 - Data Driven Enterprise
This is the highest level of data practices in our list.
At this level the amount of available information and org wide data access makes possible for the most of employees to answer pretty much any question about their company business. But same time it is equally important that not only infrastructure but also people are mature enough to be able to take the value from data in their daily duties.
Most successful employees are emerging to data leaders inside cross-functional teams focusing on tremendous product improvement. And not only improvement - but finding a space for a new products which will be able to conquer the market.
While on previous stages organisation was focused on quality of processes and collaboration, this stage puts people in a centre as a key factor to create outstanding product value. Same time processes are intended to empower people and provide right data context required for optimal decisions making in every specific case.
So in this new organisation - business processes and collaboration are no longer driving factor. They become just an extra data context to support the main goal - make people to create a value through product innovation.
Data Driven approach impact to the core KPIs:
|Processes||Improved relevance by making correct decisions with the help of rich data context|
|Collaboration||Department level collaboration is replaced by team level collaboration|
|Product||Cross-functional teams powered by org-wide shared data make tremendous impact directly to the product development life cycle|
Let's summarise how typical enterprise benefits from data driven approach in a short easy to remember table.
|Automation||Data Awareness||Data Driven|
|Processes||Improves quality||Improves time to market||Optimised to focus on real problems based on rich data context|
|Collaboration||Decreases gaps||Faster collaboration through data sharing||Department level collaboration moved to team level|
|Product||Improves predictability, decreases human factor||Quantitative improvements by X%||Cross-functional teams of experts directly impact product development life cycle|