Part of the Enterprise Data Strategy series, which explores the importance of leadership and accountability in directing an overall data strategy tied to business outcomes.
It’s impossible to have a successful, outcome-driven enterprise data strategy without a technology roadmap, a strategic view of the tools and technology needed to enable your business priorities. Technology touches on every other aspect of your data strategy – or at least it should – which is why we saved this topic for last in our enterprise data strategy series.
Developing a solid technology roadmap requires understanding your “current state” of technical readiness – where you are, what you see as a priority, and how you want to get started. Once you understand where you are (gaps and opportunities) and you’ve identified where you want to go (business objective), it’s easier to ensure that the components of your technology roadmap are in sync with your overall data strategy. Below are some examples of the types of information you can include:
- Architecture and design: The design, flow, and catalog of information and how it’s documented, managed, and governed across the enterprise
- Enterprise information management: Data governance, quality, integration, etc.
- Data sources and storage: Data warehouses, third-party data sources, in-memory databases, data lakes, etc.
- Data analysis: Data quality and process monitoring, reporting and dashboards, analytics, artificial intelligence (AI), and machine learning
Automation should be a big part of your technology plan. It helps accelerate the effectiveness of your data strategy while also improving data quality and enabling digital transformation. In fact, Forrester predicts that in 2019, “automation will become the tip of the digital transformation spear, impacting everything from infrastructure to customers to business models.”
Why are tools and technology important?
As I noted above, it’s impossible to have a successful data strategy without a technology roadmap that defines the required solutions, use cases, and implementation plans. It’s the foundation of your strategy, the skeletal system. It gives your outcome-driven data strategy its shape and facilitates its execution.
Technology areas to consider include:
- Business information and process modeling
- Data orchestration for central management of data transfers and rules
- Data quality, including profiling, monitoring, and both batch and real-time validation and cleansing
- Master data management of critical data domains with the ability to directly embed them within your business process tools
- Remediation and reporting for self-service analytics and application building
- Lifecycle management tools for retention and archiving policies that are regulatory-compliant
Each of the above technical solutions serves multiple use cases, like analytics, transactions, and applications, and they should all work together. As you’re asked to do more and more to manage data, new technologies like artificial intelligence, predictive analytics, blockchain, experience data, and more will also be key. Having a flexible roadmap that allows you to adapt to changing business priorities is critical to the success of your data strategy.
What are the keys to success?
- Align your technology roadmap with your data strategy. See “gotchas” below.
- Think big picture. Your roadmap must be enterprise-wide. Make a point to look for opportunities to reuse solutions across the business and break down silos.
- Be up to date on the latest technology offerings. How can you leverage the newest intelligent technology (artificial intelligence, machine learning, Internet of Things, blockchain, etc.) to optimize your processes?
What are the “gotchas”?
The biggest gotcha – the one that happens most often – is separating your data strategy from your technology roadmap. They need to align. Period. The other good lesson to take from others’ mistakes: Think holistically and consider the enterprise architecture and how data solutions and tools sync with the overall technology vision. Then don’t do too much at once. Prioritize.
How do you get started?
As I noted at the outset, the first thing you need to do is understand your current state:
- What are your current technical capabilities and architecture?
- What data is your highest priority?
- Where are your greatest data challenges?
- Where are the gaps and opportunities to automate?
The answers to these questions – along with your overall business goal – is where you start. From there:
- Work on the upfront design. Before you start exploring your options, you need to answer several questions from a holistic perspective. Let’s use IoT technologies (IoTT) to illustrate. Some questions you might ask/answer are:
- What do you want to collect from IoTT?
- How will these IoTT data fit with the existing data you’re managing? How will you integrate it?
- How will storage work? Where will the data persist?
- How will data be updated? Who will have access to it?
- What are the required data standards to uphold?
- Prioritize. Use the answers to all the questions related to the current state and design to determine your execution sequence.
- Don’t forget the acceleration component. Automation can improve data quality, usefulness, collection, and consumption, so as part of the roadmap, determine where it makes sense to automate. For example, automated data quality should be included in the early stages of the plan to deliver business value faster.
For more information
- Read all five blog posts in the Enterprise Data Strategy series
- Check out the Data Strategy Guidebook: What Every Executive Needs to Know for more details
- Gain a better understanding of your current organizational maturity with SAP’s executive-focused, next-generation Database and Data Management Assessment, which is based on top KPIs and best practices
- Reach out to us (Maria for North America and Tina for everywhere else) to inquire about a 1:1 enterprise data strategy discussion
And please listen to the replay of our “Pathways to the Intelligent Enterprise” Webinar, featuring Phil Carter, chief analyst at IDC, and SAP’s Dan Kearnan and Ginger Gatling.