Becoming a Data-Driven Enterprise through Analytics

Shifting to Value-based Data & Analytics

Introduction

Dashboards are dead. We have heard this for years, yet leaders continue requesting Dashboards to manage their businesses. Leaders request dashboards because they are popular, even if they get a bad rap as low-value assets. Dashboards represent a polished (or not-so-polished) presentation of operational data, serving as the entry point to a world of KPIs. But their use typically ends there. Analysis, especially interpretation, corrective action, etc., are supplemental and disconnected. Further, dashboard maturity typically peaks with advanced analytics, such as predictive models, but we still see a heavy focus on the dashboard as the holy grail.

This is not to say using dashboards is wrong; it is just that we are in a rut. But let’s be more precise - there will always be a need for operational reporting and reviewing the past. This can be done in various ways, but dashboards are the frontrunner. The reality is that leaders need questions answered every day for repeatable and non-repeatable reasons. Whether a leader receives a dashboard, a report with columns and rows of data, or a mobile app, the delivery vehicle should be less important than the value of what is being delivered. And how do we get out of this rut?

Simply put, do analytics drive a strategic business outcome?

To recognize this, we must shift to value-based analytics, prioritize data and analytics functions and connect them to business value.

Thus, as data and analytics leaders, we must help tie analytics needs to measurable business outcomes and company objectives. Data and analytics should be considered equal partners in the business value streams. Additionally, we must strive to automate, reduce inefficiencies and build a reliable, repeatable, and frictionless process where we move data closer to the business.

This shift requires a culture change. We must improve data literacy skills and introduce complementary technology (read: composable, low/no-code, AI), and we should shift our thinking from a technology-first to a business-value-first approach. And we must challenge our teams to put data-driven principles into practice and multiply those in the organization.

The Three Value Propositions

To begin the journey, there are three fundamental value propositions (as outlined by Gartner

¹

), which I believe serve as the starting point for executing this strategy:

• Utility

  • Data is instantly available, from anywhere, on any device, for all (Democratization)

  • Measure through SLAs

• Enabler

  • The business can make the right decisions every day through data and analytics

  • Measure through business KPIs

• Driver

  • Data and analytics drive the business forward

  • Measure through innovation initiatives

The Power of a Data-Driven Culture

A data-driven culture, one where decisions are based on data rather than intuition, must be amplified to sustain this shift to value-based analytics. To create this culture, organizations need to incentivize data-driven decision-making, encourage collaboration between departments, adopt an agile approach to problem-solving, provide access to the right tools and technologies, and embrace experimentation and learning from mistakes.

Benefits of a data-driven culture:

• Improving critical decision-making through data and analytics transparency

• Uncovering hidden opportunities or risks

• Enhancing customer experience

• Improving operational efficiency

• Reducing costs leads to increasing profitability

• Strengthening the skills of the workforce and the nervous system of the organization

Data Literacy and Its Importance

The average citizen does not understand data. It is not their fault necessarily, but rather from a lack of data education and poor communication. We hear data discussed, but often it lacks context, source attribution, or impact. Improving our understanding of data, better referred to as data literacy, will equip society with the right skills to read, write and communicate data. Data literacy is a powerful skill for businesses to make informed decisions and drive change.

The book Data Literacy by Aiken and Harbour outlines four data truths, which, as an organization strengthens its data literacy initiatives, should become part of the curriculum:

1. Data volume is increasing faster than organizations can process it

2. Poor data and analytics practices divert resources and reduce productivity

3. Reliance on technology and current education practices has not materially improved experiences and bottom-line improvements

4. Data requires organization

Taking these truths into perspective, there then needs to be a concerted effort among data and analytics practitioners to prioritize their team’s work around lessening the impact of these truths through effective education, leading value-first assessments of requirements, and IT operations. It is equally essential to multiply this information into the business community to give them the right lens to view how best to use data and analytics.

Democratization of Data and Analytics

Once an organization begins its path toward improved data literacy, there will be a thirst for data and analytics.

While driving the culture and data literacy skills, it is essential that we also set up the data and analytics utility through the value-first assessment.

We must continue the disciplined value-first approach to ensure only the highest-valued analytics receive investment. But, after this is complete, the robust self-service environment will function as an enabler for the business to access and analyze data with little to no recurring IT involvement. This democratization has benefits, including the following:

• Moving data and analytics closer to those who own and create it

• Guardrails and secure access to reliable data

• Reduction of manual data processing

• Enablement of distributed decision-making

• Transparent value and value chain alignment

However, we must identify and mitigate risks associated with the democratization of data and analytics. Like in society, distributed decision-making leads to more ideas but can also lead to increased noise, prioritization conflicts, and duplicative efforts. The data democratization efforts should be balanced approaches with proper data governance strategies. Additional risks include the following:

• Shadow data and analytics functions that do not uphold the principles of a data-driven culture

• Governance models are too lenient and can lead to improper data access and usage, duplicate efforts, and wrong results

• Insufficient tools could lead to frustration and a reversion to previous models

Conclusion

Becoming a data-driven organization is essential to drive value, increase agility and compete in the marketplace. For this change to be effective, it must begin with an increased focus on understanding data (data literacy) and the importance of using data when making decisions (data culture).

It is not enough to have access to data or even the technical capabilities needed to analyze it; every person in an organization should understand why data is necessary and how its use can lead to improved outcomes.

Additionally, organizations must understand the value of data and analytics and the alignment to company goals and strategic objectives. With these critical elements present, a pervasive shift towards utilizing evidence-based decision-making can occur across all levels of an organization, and a data-driven enterprise will emerge.

1. https://www.gartner.com/en/publications/the-it-roadmap-for-data-and-analytics