Part 2 in our 3-part series on Transformational Analytics
In Part 1 of this series we explored the definition of Transformational Analytics, as well as some considerations in preparing for your journey.
Business people routinely and easily obtain information that they can customize, which helps them run and continually improve the business.
In Part 2 we will take a deeper dive into what Transformational Analytics looks like in the real world and develop framework that gives substance to the definition.
Before getting too far ahead of ourselves - it must be noted that no cookie cutter approach will work for everyone.
HOWEVER, each journey will usually take place within a similar framework - consciously or unconsciously. This article is intended to help you down the conscious path with a framework that has a successful track record. One that addresses the entire spectrum of analytics activities.
To facilitate this common framework by which we can plan for the journey, let's look at the components that make up a robust Analytics function - one that is designed and built to transform your entire business.
Analytics Delivery and Execution
At the core of any analytics function are the elements for the Execution and delivery of insights:
Your data is at the center of your analytics.
Within the maze of all that data are the insights you need to power your business.
Between data and insights are the technologies: a data warehouse, ETL tools, a data science platform, data visualization tools, etc.
Leveraging those technologies are the people who architect, engineer, analyze and consume the data and insights.
As a side note - this is where the area of Data Operations comes into the picture - aligning the data, technology, and people to deliver business insights. This is a very important topic that we will explore in a separate article.
The Execution and Delivery components of your analytics function will generally take up the majority of your overall analytics resources and budget. And rightly so - as without the right data in the right format that can be accessed for analysis, your transformational analytics journey is a non-starter.
However, if this area is taking up 100% of the resources and budget, you will not be in a position to reach the full potential of your data, technology, and people.
To get a better understanding why this is a truism - let's look at a few examples of real-world situations. Do any of these sound familiar?
Your analysts do not have access to all of the necessary data
You spend more time arbitrating over the numbers than analyzing them
There is confusion over metrics/KPI definitions and calculations
Data quality issues are becoming more common and take significant efforts to resolve
A lack of data privacy and security policies are putting you and your company at risk
When you face these and similar issues, the trust in your data and analytics erodes. Left unchecked, even with the best technology and people, your analytics function will fail.
So there needs to be something more to this framework....
Data Governance / Enablement
The key to building and keeping the trust in your data, analytics, and insights is Governance of the analytics Execution and Delivery:
For many people, Data Governance is a four (teen) letter word (phrase). This bad rap has come about as a result of overly zealous, rigid and overbearing efforts. With the exception of legal/security/privacy policies, it does not have to be, nor should it be, this way.
A properly implemented Data Governance function can and does enable and empower analytics. It actually speeds up the analytics by ensuring trust in the insights. Ensuring access to the right data; that the proper calculations are applied to metrics; and that data quality issues are quickly identified and addressed.
As organizations mature, they will usually formalize the governance activities - hiring a person or team to oversee these activities. Smaller organizations will typically have an analyst manage the critical elements of governance as a "side job".
Either way, organizations quite often stop there - having built out robust but nimble capabilities for analytics delivery, execution, and governance.
However, this is ultimately insufficient to achieving Transformational Analytics....
Analytics Operations
The single most overlooked area in analytics are the "behind the scenes" activities. What I refer to as "Analytics Operations" - the glue that ties everything else together and drives operational efficiency and excellence.
There is very little discussion about this in conferences, trade publications, etc. It just isn't cool enough to draw many attendees to the breakout, or page views.
So - why is Analytics Operations so important? Well, how many of these real-world situations can you relate to?
Notifications for maintenance or outages are sent to the wrong people (if they are even sent at all)
Processes for getting access to dashboards or data sets are inconsistent (at best), unknown, or do not formally exist at all.
Efforts by the various analytics teams are unknowingly duplicated or conflicting
There is a lack of awareness / visibility into analytics projects and their impacts to other efforts
Rogue databases keep popping up
Prioritization of analytics requests is contentious
These are just a few examples of operational issues that simply do not get enough attention in virtually ALL organizations.
While arguably not as critical as governance, a lack of analytics operational excellence results in significant wasted time and resources, frustrated analysts and business users, and the undermining of your entire analytics function.
Here is what Analytics Operations looks like in our framework:
Key components of Analytics Operations include:
The processes and workflows that streamline data access requests, managing your analytics stakeholders and the communications with them.
The cross-functional alignment, planning, and change management of analytics activities and projects.
Stakeholder engagement and enablement. Think of it as the Customer Success function for your internal stakeholders. Are they aware of all the capabilities? Are they maximizing the value from generated insights? Do they need analytics onboarding for newhires? Are there opportunities for aligning their efforts with other teams?
Some larger organizations may have a PMO or Operations team to handle these activities, but more often than not, this role is handled informally on-the-side by various people across the organization - usually without any coordination or alignment. This predictably results in inconsistencies and inefficiencies that undermine operational excellence.
Check out "The Most Important Piece Missing From Your Analytics" for a deeper-dive into this topic.
OK - so now we have identified 3 elements of our framework for Transformational Analytics: execution, governance, and operations - all of which combine to help ensure an efficient organization that delivers trusted insights to business users.
Analytics Strategy
However, there is one remaining component. The one thing that ties everything together and whose existence provides the basis for the day-to-day activities: a well-defined, clearly communicated, comprehensive, and scalable Analytics Strategy.
What can happen without this? Well, similar to a non-existent or unclear overall business strategy, the lack of a robust analytics strategy can and will result in these types of issues:
Proliferation of redundant and/or incompatible analytics technologies: multiple databases, data viz tools, etc.
Random or sudden changes in direction - often without any prior communications
Politics affecting decisions about the data, tools, projects or organizational structure
Unclear priorities causing confusion and resulting in delays in moving forward on important projects or hiring
How does one develop a successful Analytics Strategy?
We'll deep-dive into this topic in an upcoming article, but here is a sneak peak....
Assessing where your organization currently stands in terms of analytics capabilities, operations, and the ability to meet the needs of the business.
Understanding the corporate business strategy, goals, and plans
Understanding the business needs for data and analytics
Overlaying the above three items to paint a picture of what is working, what isn't working, and what is missing from your current analytics efforts
Aligning the organization on the desired end-state picture of what "data driven" means
Documenting a framework (not the details) of how (not what) the key aspects of analytics capabilities (technology, process, people) will empower the business with insights to drive decisions and strategic direction.
And thus, our complete Transformational Analytics framework looks is finally complete:
So, while Analytics Strategy appears as the last piece in our framework, it should be noted that it is actually the FIRST component that should be defined / refined / finalized.
As such, we are naming this framework "SOGE":
Strategy
Operations
Governance
Execution and delivery
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