We provided some guidance in a previous post on How To Assess Your Company’s Data & Analytics Capability. This was in light of recent survey findings published in Harvard Business Review that found that a large majority of business leaders don’t feel like their organisations are still hitting the mark in regard to data and analytics. Following on from that, in this post we’ll provide some practical tips on how to enhance business engagement across these areas of capability for effective data and analytics to get you started on the road to improvement.
1. How To Enhance Cultural & Business Alignment
Executives often cite culture as the greatest impediment to becoming data driven. A data-driven culture is one where there is a single version of the truth for all performance related data that users can access and analyse. Users expect decisions to be supported and validated by data and users should have freedom and be encouraged to explore and analyse data to answer their business questions. So how can we work towards achieving this:
- Senior leadership needs to lead by example, making informed decisions using data and requiring it of others
- Use data and analytics experience and interest as a criteria when selecting business leaders
- Help employees become comfortable making decisions based on data. Put in place appropriate data literacy and technology training and provide staff with the tools that encourage data self-service and exploration
- Underscore the value of data and analytics by linking data driven decision making to compensation, rewards and recognition
- Integrate data, analytics and insights into daily workflows at the point of decision making eg. weekly sales meetings
- Demonstrate how the organisation’s business strategy is supported by its data strategy, complementing business strategy rather than replacing it
2. Strengthen Leadership Commitment
Executives and the business as a whole need to understand the importance of investing in effective data and analytics programs. The quantifiable competitive advantage it can drive and how it can deliver more informed, timely decision making. Too often data and analytics projects can fall over because of a lack of leadership commitment from within the business, so getting this right is important. To improve this level of commitment:
- Reference competitive use cases or case studies that are visible and relatable throughout the organisation
- Understand the leader’s pain points and show how that problem can be addressed through data – performing a proof-of-concept often helps here to make it more tangible
- Break up the data & analytics program into smaller, more manageable parts to prove value in a specific business area
- Continually educate on the business benefits of being more data driven
3. Optimising Operations & Structure To Support Access To Data
This is about ensuring the organisation has the right people, processes and technologies in place to provide access to data for those that need it.
- Democratise data access across departments without losing sight of privacy, security and compliance considerations
- Stress the importance of harmonising systems use across teams and levels for data and analytics to encourage collaboration
A more detailed view of some of the areas that should be put in place are highlighted in the diagram below.
4. Enhancing Data & Analytics Skills & Competencies
Enhancing data, analytics and business intelligence skills covers both internal and external resource to ensure you put together a high performing team by hiring or engaging with the right people with the right skills and experience. There are a number of distinct business and technical roles and responsibilities that need to be clearly defined upfront for a data and analytics project to succeed – it’s a team effort across the organisation. These roles are shown below:
5. Technology
The final area is to ensure you have fit-for-purpose data and analytics technology platforms that support and can adapt to meet the evolving needs of the business. Consideration should be given to:
- The data sources – which are streaming & which are batch processes. The granularity of the data & how often the data should refresh
- Consider the users – how will content be consumed by users. Technical architecting of the solution should enable self-service reporting and trusted content
- Re-usability – think about placing calculations in the back-end vs the front-end
- Performance – ensure the solution is performant during peak consumption times, consider the location of users in relation to the data centre to pre-empt latency issues
- Security – Understand how you want to implement row & object level security as well as considering backup & disaster recovery plans
- Commercials – The architecture approach should give consideration to how it will impact commercials and licensing
Each business will be somewhere along the spectrum of their data and analytics journey. While there is no final destination there will always be more to do. As indicated in this and our previous post there is however a roadmap for efficiently and strategically progressing on this journey that BoomData can help you with. Assess capabilities and address areas of weakness and enhance strengths, with a view to ultimately drive competitive advantage.
