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​​Why data monetization is irrelevant for a majority of enterprises

There has been tons of discourse with regards to where the Data and Analytics organization should report. Decent anecdotal insights, but bolstering these convos with some raw data is the need of the hour. A recent LinkedIn poll that was conducted, in which more than 2000 people participated, asked the question: “On the basis of your experience in various organizations, where does the Data and Analytics organization typically report to in present day?” 

CIO (Chief Information Officer) came in first with 54% of the respondents choosing this option, and the other options of CEO/General Manager, COO, and CFO came in with 21%, 14%, and 11% respectively. 

While these poll outcomes are somewhat of a let down, they at least assist in understanding why a data monetization discussion for most enterprises is a complete waste of time. 

From the results, as specified before, we learn that in 54% of companies, the Data and Analytics organization goes about reporting to the Chief Information Officer (CIO). The data monetization discussion is doomed when the data and analytics organization provides its reporting to the CIO. Why is this the case? This is due to the fact that the Data and analytics initiatives are then viewed as technology initiatives, not business efforts, by the management. And if data and analytics are perceived as tech efforts and not overtly concentrated on deriving and driving new sources of client, product, and operational value, then there is no data monetization discussion that needs to be undertaken. 

The following are the reasons why the Data and Analytics functions ought not to provide their reports to the Chief Information Officer (CIO) 

  • The CIO’s main area of concentration is on keeping the operational frameworks – ERP, HR, CRM, SFA, MRM, BFA, up and running. If one of these frameworks are rendered unusable then the organization crawls down to a stop, no orders are taken, no products are services are up for sale, no components get produced, etc. Making sure that these systems never stop functioning, and are out of harm’s way from hackers, cyberattacks, and ransomware is top priority for the Chief Information Officer. Unluckily, this implies that data and analytics are of second priority in the perspective of the CIO as the data and analytics are of reduced criticality to vital operations of the enterprise. 
  • And while everybody is eager to pinpoint that the CIO usually has accountability for the data warehouse and Business Intelligence, the Data Warehouse and BI systems mainly exist to assist the management, operational, and compliance reporting requirements of the operational systems.
  • Lastly, the lead of the Data and Analytics organization requires to be equivalent to the CIO with regards to the senior executive discussions and decisions with regards to prioritizing the company’s technology, data, and analytics interests. If the CDAO reports to the CIO, then the investments made in data and analytics could easily take second or third priority to the operational system investments. 

To be completely honest, packaged operations systems are merely sources of competitive parity. It’s really difficult to distinguish your organization when everybody is executing identical SAP ERP, Siebel CRM and Salesforce SFA systems. Additionally, nobody purchases your products and services owing to the fact that you possess a better human resources or finance system. 

Therefore, enterprises must elevate the part of the data and analytics organization if they are looking to harness their data to obtain and drive fresh sources of client, product, and operational value. Consequently, the argumentation surrounding why the Data and Analytics function (or CDAO) ought to report to the CEO, General Manager, or COO consist of the following points: 

  • Just like oil was the fuel that propelled the economic expansion in the 20th century, data will play that part in the 21st century. Data is no more just the leftovers or byproduct from a company going about its routine operations. In an increasing number of industries, data is the core of the business. Therefore, the CDAO role requires to be given more importance as an equivalent in the line of business executives to illustrate the mission critical nature of data and analytics. 
  • One of the largest hurdles for the Data and Analytics function is to compel collaboration across the enterprise to detect, validate, value, and prioritize the organizational and operational use cases against which to go about applying their data and analytics assets. Data and analytics initiatives don’t fall flat owing to lacking use cases, they fall flat as they have too much. As an outcome, enterprises attempt to peanut butter their restricted data and analytics resources through too many use cases leading to the outcome of inadequate performance levels in each of them. The Data and Analytics function requires to be treated as equal within the C-suite to strategically provide priority to the development and application of the data and analytic resources.
  • A critical matter of concern for the Data and Analytics function is to obtain new sources of internal and external data that improves the data coursing through the operational systems. These are data sources that don’t usually tickle the fancy of the CIO. The Data and Analytics function will be blended into these data sources to uncover, codify, and improve the client, product, and operational insights on an ongoing basis, which are forecasted propensities – through several business and operational use cases.
  • It is critical that all enterprises produce a data-based/analytics-empowered culture where everybody is empowered to envision where and how data and analytics can obtain and drive new sources of value. That kind of empowerment must trickle down from the very top of the organizational structure. Grassroots empowerment attempts are critical but eventually it is up to the CEO and/or General Manager to produce a culture where everybody is empowered to look for opportunities to exploit the unique economic traits of the company’s data and analytics. 

The appreciating criticality of the AI Innovation office 

To completely exploit their attempts at data monetization, cutting-edge enterprises are developing an AI innovation office that is accountable for: 

  • Evaluation, validation, and training on new ML frameworks 
  • Professional development of the company’s data engineering and data science staff 
  • “Engineering” ML models into composable, reusable, refining of digital resources on an ongoing basis which can be re-used to quicken up time-to-value and de-risk use case implementations. 

The AI innovation office usually assists a “Hub-and-Spoke” data science organizational structure where: 

  • The central hub data scientists team works together with the business unit “spoke” data scientist teams to develop composable and reusable data and analytics assets. The “Hub” data science unit concentrates on the reusing, engineering, sharing, and the ongoing refinement of the enterprise’s data and analytics assets which includes the Data Lake, analytic profiles, and reusable AI/ML frameworks. 
  • The decentralized “spoke” data science unit works closely with its business unit to detect, define, produce, and deploy AI / ML models in assistance of optimizing the business unit’s most critical use cases. They leverage a collaboration-based engagement procedure with their relevant business units to detect, authenticate, value, and prioritize the use cases on which they will concentrate their data science capacities. 

The AI Innovation office can assist a data scientist rotational initiative where data scientists cycle amongst the hub and the spoke to furnish new learning and professional development avenues. This gives the best in data science “organizational improv” in the ability to shift data science team members within projects on the basis of the unique data science necessities of that specific use case. 

Lastly, another vital activity for the AI Innovation Office is to carry out a sponsorship for the enterprise’s Data Monetization Council that possesses the corporate mandate to drive the sharing, reuse, and ongoing refinement of the company’s data and analytic resources. If data and analytics are really economic resources that can obtain and drive new sources of client, product, and operational value, then the enterprise requires a governance organization with “stick and carrot” authority to enforce the ongoing cultivation of these vital 21st century economic resources. 

A primary goal of the Data Monetization Governance Council is to end information silos, shadow IT expenditure, and orphaned analytics that have the effect of a drag on the economic value of data and analytics. With regards to governance, to be efficient, it requires teeth. Governance must consist of recognition for compliance – for e.g., assets, investments, executive focus, and budgets – in addition to penalization for non-compliance – for e.g. holding back or even clawing back assets, budget, investments, and executive focus. If your governance methodologies is reliant on cajoling and pleading with others to achieve compliance, then your governance practice has already faced failure. 


So, in conclusion, for a majority of enterprises, the data monetization discussion is a complete waste of time resources as data monetization discussions do not begin with technology but begins with the business. This implies that the Data and Analytics function must have a consideration within the C-suite, else the data monetization discussion really is a complete and utter waste of time. 

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