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Trying to run before you can crawl? What your data roadmap may be lacking

Trying to run before you can crawl? What your data roadmap may be lacking

Trying to run before you can crawl? What your data roadmap may be lacking Trying to run before you can crawl? What your data roadmap may be lacking
Marketing Analyst, Marketing Science & Applied Analytics Practice
Valtech
Blair Roebuck

December 09, 2019

For many years, the data space has been the Wild West: lawless and chaotic. In this chaos, organizations scraped and collected as much data as they could get their hands on, without clear consideration for what they were going to do with this information.

As such, we see countless companies today drowning in a sea of data, unable to drive actionable insights. There is an inability to decipher value from vanity, and as such, failure to create actionable programs. In fact, in a recent New Vantage Partners survey, nearly all respondents (99%) said their firms are trying to make a shift to a data-driven culture, but only one-third have succeeded at this objective. How does this happen? In this article, I will outline the five major pieces that companies “lack” in their data roadmaps and how to overcome these hurdles.

1. Lack of Education

To ensure a strong data program, you need a team that is data literate. To ensure they are data literate, you need your team to understand the tools available to you, the data it collects, and how your data ecosystem communicates.

Many clients struggle with a lack of knowledge about the tools they have at their fingertips. As a result, they are unable to leverage their in-house capabilities and spend unnecessary dollars investing in redundant or unnecessary tools.

How to overcome this:
  • Start by conducting a tech stack audit of all tools being used and their intended purpose.
  • Identify any duplications/redundancies in your suite of tools.
  • Once you have identified what tools you currently have and their purposes, provide hands-on training of how to use the various tools, and how the various systems can communicate to leverage out of the box capabilities.

2. Lack of Strategy

The downfall of any data-led initiative is starting it before you have an agreed upon strategy. Collecting data without a direct correlation to business objectives & goals is like embarking on a road trip to a new destination without checking the route: you’re bound to get lost and frustrated.

Ensuring that there is a cohesive understanding of your organizations business objectives, marketing initiatives, key performance indicators & fiscal targets is the foundational element to any successful data program. Your strategy is your GPS: helping you get to your destination. Even if you stray off the path, your strategy will bring you back to where you need to go.

How to start building your strategy:
  • Identify your organizations business objectives, marketing initiatives, key performance indicators & fiscal targets. Doing so will ensure you are collecting metrics of merit & build a strong data foundation.
  • Answer this question: what are the key business questions your CEO wants answers to?

3. Lack of Visibility Across Business Unit Lines

The power of data is ubiquitous. In today’s digital ecosystem, every facet of a business has valuable data to be shared. The common misstep is a lack of communication across business unit lines. As you may find during your tech stack audit, two business units may be using two tools for the same purpose, which is an inefficient use of company resources and time.

Moreover, your department may be collecting fantastic data that can positively impact that way that another department conducts their day-to-day business. Breaking down silo’s and having an honest conversation about what data you have, need, want opens the floodgates for collaboration.

How to break down these silos:
  • Conduct a gap analysis – what data are you currently missing that would help answer key business questions? Reach out to different business unit lines to see if they have the missing pieces.
  • Host knowledge sharing sessions to educate your departments about the data you are collecting, what you’re doing with this data to enact change, and how the other business lines can benefit.

4. Lack of Foresight & Planning in the Era of GDPR, PII & California Consumer Privacy Act

2018 was a transformational year for data legislation with the introduction of GDPR, PIPEDA & the California Privacy Act. These acts are designed to protect the rights of the private citizen and their personal data.

Although many of these laws appear regional, do not be fooled: they affect everyone, and it is a dangerous assumption to think otherwise.

Ensuring that the data you wish to collect is legally sound, as well as implementing regular legal & data check points will ensure the unveiling of the latest data privacy legislation does send your business into a tailspin. Knowing what data you have, where it’s stored (which you will achieve during your tech audit & infrastructure mapping in “Lack 1”), and continual refinements will reduce the fear of the increasingly regulated data space.

How to better prepare for legislation:
  • Engage your legal team early in the planning process. Be prepared to discuss what data you would like to collect on users, why you wish to access this data, and how it is stored.
  • Do routine data sweeps, cleans & maintenance to ensure your dataset is up-to-date and up to regulation standards.

5. Lack of Governance

Knowing what we want to do and how to do it are crucial, but in the midst all this planning, companies neglect a huge piece: who will do this work. Assigning clear roles ensures proper execution from technical set up, to workflow, and reporting must all be considered during the planning stages, to ensure seamless execution.

Governance is successful when the team identifies what needs to be completed, and who is best suited to execute. Being best suited for the task goes beyond skill set or job title. Knowing the strengths, weaknesses, likes and dislikes sets your individual team members up for success, and fosters team collaboration.

How to build a governance model:
  • Start by listing out the action items
  • Have each team member list out their strengths, weaknesses, likes, dislikes
  • Create a RACI Chart with the action items, and assign various levels of responsibilities to each member given their strengths, weaknesses, likes, dislikes previously identified
    • R – Responsible: The “doer” is the individual(s) who complete the task.  The “doer” Is responsible for action/implementation. Responsibility can be shared. The degree of responsibility is determined by the individual with the “A”.
    • A – Accountable: The accountable person is the individual who is ultimately answerable for the activity or decision.  This includes “yes” or “no” authority and veto power.  Only one “A” can be assigned to an action.
    • C – Consult: The consult role is individual(s) (typically subject matter experts) to be consulted prior to a final decision or action. This is a predetermined need for two-way communication.  Input from the designated position is required.
    • I – Inform: This is individual (s) who needs to be informed after a decision or action is taken.  They may be required to act as a result of the outcome. It is a one-way communication.

Data is a constantly evolving space. With the right approach, it can elevate your business to new heights, but when mismanaged, it can cause headaches and slow down business process. It is important to constantly revisit these five “lacks” to ensure your team is up to date with your business needs, team skillset, and industry demands.

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