Blog > Data Integrity > Turning data issues into opportunities: How to start a data project

Turning data issues into opportunities: How to start a data project

David Woods | January 19, 2022

Initiating a data program can take many forms and is often a daunting endeavor, but the reality is that starting these programs is a critical foundational investment that helps your business continue to grow and flourish. As the Senior Vice President for Strategic Services here at Precisely, I work with companies to overcome their data issues every day, empowering them to simplify and align to top-level brand messages and make more confident business decisions with data they can trust.

I recently sat down with Ian Murphy of the Enterprise Times for a podcast to discuss how we’re helping organizations design, develop, implement, operationalize, and ultimately optimize their data programs.

Whether the focus is on data quality and data governance, data lineage, data science, or data analytics, we at Precisely are here to help you define that path to not only get started, but improve your programs over time and deliver measurable value.

data issues - where do you start with a data project

Addressing data issues and beyond

When we’re asked to engage with an organization, there’s usually a specific call to action involving a specific set of data challenges that aren’t often well-defined. Unfortunately, it usually tends to be a negative call to action, and not necessarily an opportunity. Part of the challenge and a key aspect of what we drive with organizations is to make sure that we can address the immediate call to action – the burning data issues – but then be able to work our way up to true value creation.

Let’s take a look at how we get started, and a snapshot of how we can help you through that process.

Program framework goals and objectives

 The best way to drive immediate and long-term value is to leverage a proven framework that captures organizational goals and objectives and discretely ties them to the underlying data challenges and opportunities. This certainly incorporates a lot of leading practices, but largely involves targeted discussions that get to the root of what you need, dissecting questions like:

  • What specific outcomes are you trying to achieve and what does success look like?
  • What’s required to achieve those outcomes from a people, process and technology perspective, and what specific data is critical to ensure they are achieved and sustained?
  • What data capabilities need to be in place to make sure that we not only address the issues, but transform them into opportunities and value for your organization?

These discussions cannot be bypassed and are pivotal as we begin to look at your data and the corresponding value drivers that are required to build and sustain a data program.

Listen to the Podcast

Where do you start with a data project

Starting a data project can be a little like finding a needle in a haystack while wearing boxing gloves and being blindfolded. To get a better idea of how to get started, Enterprise Times talked with David Woods, Senior Vice President for Strategic Services at Precisely. Listen to the podcast

Working to address data issues across organizational levels

Experience tells us that there are generally three distinct paths that drive data programs and projects:

  1. Bottom-up, where tactical data issues need to be resolved to address a set of specific data errors or broader challenges like a data integration project.
  2. Middle-out, where business process execution and operational opportunities require a data focus.
  3. Top-down, where executive level goals are driving data priorities and actions.

Navigating each path requires you to follow a distinct approach, but to ultimately be effective you always need to consider each level as part of the solution.

Bottom up: Addressing table- and field-level issues

Most often, projects and programs are initiated based on a bottom-up challenge and start by working with the data at a table-and-field level. Most organizations have upwards of 20,000 different data elements within their ecosystem that support their processes, analytics and system interoperability. The inherent challenge with that amount of data is knowing where to start and how to identify and prioritize the data that matters the most.

As we execute and build the program, we must make certain that while we’re solving a specific challenge, This not only drives value, but also followership and overall support for the data program.

That bottom-up path also presents overall engagement challenges because the personas that are involved tend to be the more tactical users of the data. Those data professionals are extremely comfortable talking about data in that table-field type dialogue, but that language is not easily translated to business owners and stakeholders. A framework that ties business outcomes to the data is the key to making this connection. And without it, organizations will always struggle to get meaningful and lasting business buy-in or support.

Middle out: Looking at business process execution

The middle-out path involves data challenges that are associated with some sort of business process execution – either a breakdown or opportunity. In these instances, the approach needs to follow two parallel tracks:

  1. Ensuring that we’re working our way up and translating our plan of action into things that executives actually care about. This often involves a proof of value conversation and business justification.
  2. Working our way down to the IT and business personas that work with the data on a day-to-day basis and to make sure that they not only know what needs to change, but also why it matters and how it impacts the business overall.

Top-down: Aligning to business goals

The top-down path is an ideal scenario. This is where we have a call to action that’s more organizationally focused and endorsed by leadership. Ultimately, executives really care about four or five macro-level things, usually either top-line or bottom-line focused. They aren’t in a position to understand or even particularly care about data unless it’s critical to driving specific outcomes and benefits they care about and have committed to achieve.

Initiating a data program with this call to action must first start with anchoring on those business goals. And then decomposing them into a plan for data that can always be translated back into those goals and objectives. This has traditionally been a major challenge for data teams, but the framework again helps clear the path and facilitates a data dialogue everyone can understand and support.

As an example, in today’s COVID times many leaders are trying to improve their working capital. If an organization wants benefits in this area, they have several levers to pull. For most, though, it comes down to payables, receivables, and inventory on-hand. If we work our way down from an executive-level working capital conversation, identify the performance measures we’ll use to track progress and then identify the processes that create, consume or rely on the data, we now have a model that has true meaning to everyone in the organization.

Starting a data program or project with a top-down approach is not always feasible, but it’s the preferred path as it tends to accelerate adoption because there’s clear line of sight into the value and a clear prioritization of the data that matters. The middle-out path has many positive characteristics because it inherently has transparent value to subsets of business stakeholders, but we’re generally addressing data opportunities that have a somewhat siloed impact and don’t have clear outcomes that scale across the business. The bottom-up path usually provides more immediate gratification, as we’re able to solve a specific data issue, but that feeling won’t sustain unless we connect that value up the chain in a meaningful way.

Ultimately, all three paths are viable starting points to initiate a data program, but without a framework that ties our data to business outcomes, we’ll never achieve long-term success or tangible benefits.

Data program, not project: thinking long-term

What it truly comes down to, and what we encourage everyone to do, is to think “program” not “project.” We almost always need to execute in a project mode with any data initiative, but always have a program mindset and ensure that the capabilities you build and the decisions you make can scale and are tied to meaningful business outcomes.

When it comes to addressing your data issues and starting a program, think of it this way: at the end of this project, are we in a better place with our data program overall and have we set ourselves up to continually improve and build on that success? It’s not about trying to lose eight pounds right now because you have a wedding coming up in the next week. It’s about making healthy choices to get those eight pounds off, and then maintaining that same momentum to make sure that we’re going to keep them off and get to a healthier, stronger state over time.

Ian Murphy and I covered specific tactics on these topics and a lot more during our chat on the Enterprise Times podcast, so be sure to listen to the full interview above.

Members of our Precisely Strategic Services team are well-recognized experts in helping organizations define and optimize their data programs, and maximize their data investments to deliver measurable outcomes and achieve long-term success. Learn more about Precisely Strategic Services.