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Systematic Approach To Data And Analytics Adoption

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Organizations — for-profit and nonprofit — all over the world are looking at leveraging data and analytics (D&A) for improved business performance. Findings from a McKinsey Global Institute survey indicate that data-driven organizations are 23 times more likely to acquire customers, six times as likely to retain customers and 19 times more profitable. But many companies, despite being data-rich, are poor in deriving and adopting insights. The results from another McKinsey survey show that fewer than 20% of companies have maximized the potential and achieved analytics at scale. According to research from the market intelligence firm IDC, one major reason for the poor success rate of D&A projects is the lack of stakeholder buy-in; in other words, poor user adoption.

So, how can a business enterprise improve the adoption of D&A and deliver results? Below are five key recommended strategies businesses can undertake to increase the adoption of D&A and improve business performance.

1. Focus on the right KPIs.

D&A solutions should be centered around questions and key performance indicators (KPIs). KPIs, which rely on data, enable enterprise performance management (EPM) by allowing the enterprise to track progress against the business objectives and set targets. If a business is using KPIs to measure its performance, those KPIs typically drive business behavior, results and the organization’s culture. Hence, D&A solutions should be aligned to strategic enterprise goals and KPIs.

2. Promote decentralized decision-making.

Delivering business value in the cheapest and quickest possible ways often requires decentralized decision-making. In this backdrop, business users should be supplemented with the D&A skills to derive insights, as they understand data well due to their proximity to the business activities. This will position the business users not only to effectively derive the insights but also to reduce the cycle time in converting insights to actions. In the context of D&A, every company today is a data company and, hence, every business person should be positioned as a citizen data scientist to build analytics models integral to their current job of running business operations. Decentralized decision-making, when linked to the strategic enterprise goals and KPIs, improves throughput, allows for swift feedback and facilitates innovative solutions in the organization.

3. Build the use-case library and road map.

While KPIs are associated with strategic enterprise goals and provide the business context, they are often at a high level. D&A solutions should be formulated with the right use cases at the user persona level. Fundamentally, the D&A use case is the manner in which the business user leverages data and statistical algorithms to derive insights to answer business questions for decision-making. Formulating use cases often results in an exhaustive list — known as the use-case library — that needs to be prioritized with a road map and ownership to match the short-term and long-term goals of the enterprise. The road map offers a better understanding of what types of insights and decisions are of importance to the company. In other words, a use case-based D&A road map helps to achieve measurable outcomes.

4. Deliver solutions iteratively and incrementally.

Prioritized D&A use cases enable multiple releases based on three key dimensions: returns, risk and complexity of implementation. D&A use cases related to business transactions such as orders, invoices and payroll have a high return on investment compared to static data like product categories and customer master lists. Furthermore, descriptive analytics (i.e., insights on historical performance) should be considered in the initial release to further build data literacy and enhanced adoption. Having descriptive analytics in the initial release will enable a platform to deliver predictive analytics (i.e., insights on what will happen in the future) and prescriptive analytics, which sheds light on the best course of action.

Delivering analytics solutions in an integrated manner leverages embedded analytics (i.e., assimilation of insights derived from analytics systems into transactional applications). Embedding the insights from analytics solutions into transactional applications involves bringing three key components together: a workflow for process integration, role-based access control (RBAC) for data security and application programming interfaces (APIs) for data insights.

Additionally, D&A solutions have an end of life, as business goals, needs and priorities and user preferences change. Hence, many organizations have numerous reports and dashboards that are not used effectively. In that case, these unused reports and dashboards should be retired or deprecated. As I note in my book Analytics Best Practices, D&A solutions should be delivered like a product with continuous release management, one that is constantly refined and improved for each user persona.

5. Support business users.

Just deploying D&A solutions will not fully serve the needs of the business. The changes emerging from D&A insights have to be clear and relevant so people understand what is required from them and why they need to do it. The business users and the citizen data scientists have to be supported, not only with accurate and timely data but also with the right analytics tools, training and frameworks. This will help them feel positive toward and engaged in D&A.

At its core, the adoption of D&A is about effective change management with measurable outcomes. Adoption should focus on the people and the way business stakeholders use data, algorithms, assumptions and ethics to derive insights and serve business objectives. In today’s digital and data-centric economy, D&A is a proven enabler that can transform data into a business asset by providing insights for sound decision-making and business results.

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