Re-Organizing Analytics in the Age of AI: Self-Service Analytics is the Key.

Re-Organizing Analytics in the Age of AI: Self-Service Analytics is the Key.

This is the first in a multi-part series on self-service analytics.? In this first article, we define the challenges facing Self Service Analytics.?

?AI has dramatically affected Data and Analytics, including how they’re deployed - have you noticed any of the following?

  • ?? IT and technology dominate the conversation, whereas data analytics and data science once did.
  • ?? More Data and Analytics teams are reporting to the CIO, thus increasing a platform focus.
  • ?? Marketing analytics reports to the Digital world, consolidating into the CTO organization.
  • ?? Analytics teams are focusing on more advanced and higher maturity activities such as AI applications, recommendation engines, and optimization at scale.
  • ?? Analytics tools have become more automated and have assistance features that leverage AI.

Taken together, more and more, tech-enabled analytics is replacing the dedicated resources that once provided decisioning and descriptive analytics.? As a result, there’s been substantial growth in Self-Service Analytics (SSA).

Question for business leaders:? As your analytics teams are redeploying resources to leverage AI, who’s maintaining the insights pipeline your business needs to make decisions??

Bonus points:? If your answer is SSA, are you and your organization prepared for a successful transition to SSA?

Self-Service Analytics(SSA): ??What is it?

At its core, SSA is an individual or a team focused on generating insights to make specific business decisions. Developing and implementing insights was split between analytics and business teams in prior years. Due to the trends noted above, the roles of these teams are merging, with significant challenges in cross-training analysts and business resources.

SSA:? How do we implement?

Before we begin the self-service analytics journey, it’s essential to understand the inherent roadblocks to implementation in today’s world:

  • ?? Data security and Cloud issues have not been fully considered when deploying SSA capabilities.
  • ?? SSA is not viewed as an enterprise asset. There is no single corporate owner of SSA capabilities, standards, and dashboards, potentially creating a chaotic syndrome of duplication, non-standard KPIs, views, and platforms.
  • ?? Client-centric dashboards are lacking.? Current dashboards may have too much noise and insufficient signal - lots of pretty pictures but not enough curated insights.
  • ?? Few dashboards are designed with end-user decision-making needs in mind - there is a lack of strategy for dashboard design and deployment.
  • ?? Lack of an enterprise Evergreen approach to ensure SSA is constantly upgraded with best practices.
  • ?? Some Analytics teams focused on day-to-day analytics without automating recurring requests and standardizing dashboards and common analytics.
  • Lack of training business functions on how to be good consumers of SSA.
  • Keep these roadblocks in mind as we plan our SSA journey!

Success Pillars

For a successful transition to SSA, several pillars must be cared for to maintain an operating function that is fit for purpose, is well calibrated, and aligned with the businesses it serves. We have defined 4 Focus Areas.

I.????? Data:? The data must be golden source, traced, and assessed.? Data governance and data lineage must be in place.? While lack thereof would slow down analytics in prior days, they are crucial for SSA.? Lacking these fundamentals could stop SSA or, worse, result in wrong insights for decisioning.

II.??? Platform:? Platforms and tool kits must be decided upon and standardized before implementation across the organization.

III.?? Governance:? Shared accountability for SSA success - BI/Analytics teams and the Business.?

IV.? Program Focus:? Standing up an SSA support team with technical, analytic, and business expertise.

?Potential Solutions:

?These will be expanded upon, as will the four focus areas in future posts.

·????? The need to follow a self-service analytics playbook and success framework.

·????? The need to put into place a client-centric data visualization framework.

·????? We must understand any self-service analytics organization and culture, including the talent architecture org model and data literacy required to drive a client-centric and self-service visualization framework.

·????? The need to understand how self-service analytics fits in with your broader business and marketing strategies

We look forward to hearing from you regarding the problem statement for Self-Service Analytics. Have we covered all of the issues in this post? What are the gaps and or opportunities for SSA?


Dr. Anthony Branda is the lead Partner for Marketing Data Analytics in the Advisory and Consulting Practice at TCS and the Founder of the Analytics Hall of Fame. Formerly a CAO, CDO, and CDAO for Citigroup, Commbank, RBS, Ahli-United Bank, and Embrace Home Loans.? He founded a master’s in marketing Analytics at Pace University with Tom Davenport and is published in the Journal of Marketing Analytics and Applied Marketing Analytics.

Angel Smotrytska

Token Promotion Manager ? Legal Support | Legal opinion | Audit | Listing Telegram @a_smotrytska

11 个月

The move to self-service analytics is transforming how we handle data. The challenges mentioned, especially around data security and platform standardization, are crucial. I Excited to learn more about the solutions and the self-service analytics playbook in your upcoming posts! ??

要查看或添加评论,请登录

社区洞察

其他会员也浏览了