Business analytics is the key for decision making in organization at all levels. Startups and disrupters are using data analytics - both traditional BI & Big data tools for decision making, and in many cases, automating decisions. Yet, there are several companies that are yet to fully embrace data analytics.
In our opinion, here are top 10 practices that must be followed to transform any organization into a data driven organization.
1. Start with Top Executives
Top executives must be the first ones to adapt analytics. Once top executives embrace data analytics, the rest of the organization will follow. Organizations can jump start this process by hiring a Chief Analytics Officer, who can create a data driven executive teams at the top, and then guide rest of the organization in adapting analytics. This will enable an organic growth and will take time.
2. Start Analytics Project which impacts Revenue
The first of analytics projects that are taken up must have a strong connection to bottom line. The output of the analytics must be connected to revenue or profits or costs. Projects which have such a deep & big impacts will have a higher likelihood for making a business impact and for being successful. Early success is essential to take analytics into other parts of the organization.
3. Consider organizational implications
Often in older, legacy bound organizations, cultural issues are the hardest to overcome. Even with executive sponsorship and proof of concepts to illustrate the value of analytics, there will be reluctance to adapting new technologies. Start with communicating key accomplishments & push the analytics message and continue to evangelize. This will take time, but with patience it can be done.
4. Think about Organizational design
Organizational structures in legacy companies are not designed for analytics based workflow. Hence it becomes important to reorganize to take full benefit of analytics led workflows and business processes. Reorganization will also accelerate adoption of analytics in business operations.
5. Consider new talent from outside the organization
Though it makes sense to build all the required talent in-house via training, Legacy organizations may need talent from outside the organization to build analytics skills. Alternatively, one can also consider 3rd party consultants to help execute analytics projects and accelerate deployment of analytics.
6. Move rapidly towards deployment
Analytics are valuable only when it is deployed to make decisions. Proof of concepts are good, but will be useless if it is not deployed. This means teams must organize to execute around these analytics. Successful organizations build rapid deployment of analytics.
7. Take control of your Data
Data analytics needs strong data governance & stewardship. Data management, protection and data quality - which goes a long way in building a trusted data pool (or data lake) which then can be used for analysis.
8. Track Outcomes
Track outcomes of all data analytics projects. During the initial stages, there will be missteps, constant debate on what the right thing to do. Having complete transparency about analytics projects and its outcomes helps accelerate adoption
9. Invest in new tools & Technology
Data analytics tools are rapidly evolving. New tools are being released all the time - which brings in new capabilities to create analytic models, faster analytics and AI based automated decision making.
10. Rapidly Adapt advanced analytics
Advanced analytics such as deep neural networks, robotics & automation are rapidly maturing. The advanced analytics tools work on unstructured data, streaming data, and telemetric data. These new types of data analytics will transform organizations.
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