5 Ways To Rethink Data Initiatives That Really Deliver

If data is so plentiful, why do so few data initiatives make it off the ground?

 

These days, nobody wants for data. Businesses are swimming in it, swooning over the potential and plotting the course for org-defining application. But if that’s true, why is it so hard for most businesses to deploy even the most simple reporting? In most cases the problem isn’t the data, but rather the approach.

 

Here are five ways to rethink data initiatives to ensure they get done, and more importantly, that they are effective and have a lasting impact on organizational objectives.

 

1. Think Applicably: Too many organizations start thinking about the data they have and build application from that. A lot like making lemonade from lemons, it may work in some cases, but it risks distracting from the real objectives. Just because you can report outbound calls per hour from CRM, is that the metric that is going to most support your business objectives? Effective data initiatives start from the top and work their way down. What are the objectives? What information do key personnel need to reach those objectives? What insight is missing? How do we measure both lead and lag measures? How do we define our customers and opportunities? Thinking application-down will naturally prioritize and clarify your data initiatives, while protecting against data fatigue and unnecessary distractions.

 

2. Think Collaboratively: Are IT / accounting teams the primary creative force behind your reporting? If so, you will get the safest, most reliable, and often times least effective insight available. Make no mistake, IT / accounting teams are critical in successful data deployments, but their role is supportive, not directive. If organizational goals drive intelligence needs, then you need to look to those at the tip of the spear (usually sales, marketing and executive personnel) for creative input. These individuals know what they need and are the quickest path to real, usable insight. Additionally, these stakeholders will determine adoption of the data initiatives. If it’s not useful, it won’t be used.  On the other hand, technical expertise is needed to properly define data points and sources.  Successful data initiatives are born out of collaboration between those responsible for achievement, and those who support their efforts.

 

3. Think Simply: It’s amazing the power that Machine Learning and AI provide in modern business application. But if an organization pursues ML or AI initiatives before it has committed to basic business intelligence they are committing to failure, plain and simple. Forward-thinking analytical tools like ML and AI are built on top of, not in place of, essential analytical tools. This is because they ingest high volumes of data and rely on the quality and interpretation of that underlying data to train good models. They also require massive amounts of data to identify patterns and predict future behavior. Most organizations have neither the size nor scope of data to fuel enough reliable insight to move the needle. Most importantly, if an organization has yet to demonstrate the ability to effectively apply basic data to business decisions, how can they be trusted to finally “breakthrough” with far more complex and demanding endeavors? But too often organizations invest in far-reaching endeavors while neglecting basic intelligence. Simple starts = quick gains and nurture a progressive data culture.

 

4. Think Evolutionary: This pairs well with thinking simply. It is essential that every data initiative be thought of as one step in an ever changing journey. Data exploration follows the scientific method – it is driven by questions, hypothesis, testing, etc. Questions lead to insight, which leads to new questions. On and on it goes, as it should. The trouble with some organizations is that they believe their mission is to produce only answers. They’re stuck in limbo because they think they’re missing all of the data ingredients or because they can only produce a non-conclusive answer. The right data mindset is not perfection, but value. That value should be captured, dissected, discussed, and pumped back in to the process. Get off the sidelines and start producing insight! Even the smallest nuggets will evolve into greater insight as they are used and applied by teams, who become ever more hungry as their appetite for real data is fed.

 

5. Think Urgently: The greatest threat to data initiatives is being buried by “more pressing” operational concerns. Data is king of the annual strategy meetings but pauper of the day-to-day operations. If you’re serious about data, you need to commit and you need to start – like right now. Remember – think simply and think evolutionary. More than anything else, data initiatives just need to start. If internal obligations or staffing limitations are an obstacle, enlist an outside data/intelligence firm to get you going. As just such a firm, we have done this countless times for clients. The story is almost always the same: “We’ve been talking about data for so many years, but it just never happened.” Our services provide a way for organizations to plan and deploy a comprehensive data initiative in just a few weeks. When it’s done, many comment that they can’t believe they didn’t do it sooner.

 

Any organization at any level can benefit from rethinking their data initiatives in these ways. You may just find the missing link to finally getting this data thing done!

 

Elevation Data Group is a national hybrid staffing & managed data services firm helping businesses put their data to good use.