Ten IT-enabled business trends for the decade ahead - Data Analytics

In the McKinsey Ten IT-enabled business trends for the decade ahead article the authors identify competing with data analytics as a top trend. This article resonates with me as I have been working on software projects as a project manager for the last 10 years. Almost every project has struggled with reporting requirements and effective implementation of data extraction. I am working on a project now that has faced continued problems with clear requirements and data quality.

Data analytics can help develop a customer profile so an organization can better understand the needs, interests, and behaviors of customers. This allows a company to respond more quickly to business needs. The authors say “the power of analytics is rising while costs are falling” and they contend that developing a big data plan is critical, on par with where strategic planning was 40 years ago.

So why aren’t more companies making progress here? Some items that stand out based on personal observations and reading are:

1. Lack of standardization – organizations often do not have data stewards to take ownership of the quality and lack of standards mean data elements such as “student” mean different things to different groups.
2. Data integration – building the infrastructure to integrate human resource, finance, customer, operations and other relevant data is not a priority and therefore unfunded.
3. Data quality – records are often outdated, replete with errors and the resources to clean up the data do not exist.
4. The deluge of data – organizations can struggle to get to what is important without being overwhelmed.

Finally, capturing the data is important, but understanding it is crucial. Additional work in modeling and implementing decision support tools is required to be effective. These tools come with additional costs as well as the resources needed to build and maintain them.

My takeaway was to think about how I can try and emphasize the importance of data analytics within my projects and to leadership. I came up with the following ideas.

• Identify reporting or analytical goals at project outset – establish the appropriate project success metrics as it relates to data and document risks to meeting them. Escalate the risks to project sponsors and IT leaders for more executive thought.
• When engaging IT leadership, broach the topic of data strategy – an idea that comes to mind is to relate data analytics to preferred outcomes. Gain alignment on a simple concept such as “Do we agree we want happy customers and operational excellence?” Then relate how data can help achieve those goals.
• Wins on the increments and margins – identify opportunities on software projects to clean up data or use system of record data. In my experience, the duplicate entry is a big problem and using source data from systems where data stewards already exist can help with incremental gains.

This topic is a critical factor in my current career efforts and I hope to learn more about effective planning and strategies to make organizational improvements in this area.