Outcome‑Driven Enterprise Data Strategy: The Importance of Tools, Technology, and Automation

As part of the enterprise data strategy series, this article explores the importance of leadership and responsibility in guiding an outcome‑driven overall data strategy.

Without a technology roadmap—a strategic view of the tools and technologies needed to support business priorities—a successful, outcome‑driven enterprise data strategy is impossible. Technology touches every aspect of a data strategy, which is why this topic is placed at the end of the series.

Creating a reliable technology roadmap requires understanding your technical readiness—where you are, what you consider priorities, and how you want to start. Once you know your gaps, opportunities, and business goals, it becomes easier to ensure roadmap components align with the overall data strategy. Below are examples of information types you can include:

Architecture and design: the flow, catalog, and how data is recorded, managed, and governed across the enterprise.

Enterprise information management: data governance, quality, integration, etc.

Data sources and storage: data warehouses, third‑party sources, in‑memory databases, data lakes, and so on.

Data analytics: data quality and process monitoring, reporting and dashboards, analytics, artificial intelligence (AI) and machine learning.

Automation should be a key part of your technology plan. It speeds up the effectiveness of the data strategy, improves data quality, and enables digital transformation. Forrester predicted that in 2019, “Automation will become the cutting edge of digital‑transformation pioneers, affecting everything from infrastructure to customers to business models.”

Why Tools and Technology Matter?

As mentioned, without a defined solution, use‑case, and implementation plan in a technology roadmap, a successful data strategy cannot exist. It forms the backbone of your strategy, shaping a results‑driven data approach and facilitating execution.

The technical domains to consider include:

Business information and process modeling

Data orchestration for centralized management of data movement and rules

Data quality, including analysis, monitoring, batch and real‑time validation and cleansing

Master data management for key data domains, integrated directly into business‑process tools

Self‑service analytics and application‑building reporting

Lifecycle‑management tools for retention and archiving to meet regulatory compliance

Each technical solution serves multiple use cases such as analytics, transactions, and applications, and they should work together. As demand for data management grows, new technologies like AI, predictive analytics, blockchain, and experience data become critical. A flexible roadmap that can adapt to shifting business priorities is vital for data‑strategy success.

What Is the Key to Success?

Align your technology roadmap with your data strategy. See the “gotchas” below.

Think big picture. Your roadmap must be enterprise‑wide, seeking opportunities to reuse solutions and break down silos.

Stay current with technology offerings. Leverage emerging intelligent technologies (AI, ML, IoT, blockchain, etc.) to optimise processes.

What Is the “Problem”?

The biggest problem is separating the data strategy from the technology roadmap; they need periodic alignment. Another lesson from others’ mistakes: think holistically, consider enterprise architecture, and ensure data solutions and tools sync with the overall technical vision. Don’t try to do too much at once—prioritise.

How Did You Start?

First, understand your current state:

What are your current technical capabilities and architecture?

Which data is your top priority?

What is your biggest data challenge?

Where are the gaps and opportunities for automation?

These answers, together with your overall business objectives, form the starting point. From there:

Do the Front‑End Design

Before exploring options, answer a few high‑level questions. Using IoT as an example, you might ask:

What do you want to collect from IoT?

How will IoT data align with existing data and be integrated?

Where will the data be stored?

How will the data be updated and who has access?

What data standards must be complied with?

Prioritise

Use the answers to determine execution order.

Don’t Forget to Accelerate Components

Automation can improve data quality, usefulness, collection, and usage; therefore, define the role of automation early in the roadmap so that business value can be delivered faster.

Original source: Digitalist Magazine

Article: jiagoushi.pro

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