Here’s your first question: Do you have a data strategy?
If not, then giddy-up. It’s time to jump on that horse, because there’s gold in that-there-data. If so, then you are ahead on the data strategy curve, and here are some questions that you should be thinking about to determine how good (or bad) your data strategy really is.
Question #2: Who owns your data strategy?
We’ve grouped our data strategy readiness questions into three categories, because we believe that data strategy fundamentally rests upon three basic concepts: A) Understand Your Data, B) Architect Your Data, C) Activate Your Data. These basics are really the ABC’s of pulling together a solid data strategy that can pass the test of creating competitive advantage and preparing to beat the market. So here goes…
Understanding Your Data
3) What types of data do we have and what types do we need?
Think about what you’re working towards and assess data accordingly.
4) Why are we collecting/buying this data?
Have purpose for the data you collect and purchase. The days of capturing everything—in hopes of using it sometime—are gone. Collect data for a purpose.
5) Who has access to our data?
Safeguard data. Data can be misused, abused, and misconstrued. Monitor access so data doesn’t “accidentally” fall into the wrong hands.
6) Where do we store, process, and analyze data?
C’mon, you don’t know? Aren’t sure? It’s a big question because your data is all over the place. Get a grip on it. Map it out.
7) How are we protecting PII and complying with GDPR?
Don’t think GDPR is going to impact you? Think again. You’re not insulated and you’re probably liable. But no worries, they’re not chasing you…YET. Be wary, California passed privacy laws in June of 2018, which have major implications!
8) When was the last time we took inventory of our data?
How ‘bout that dirty, messy, data? Everybody’s got dirty data. The cleanup in Row 4 may tell you that things don’t match. But the bigger problem is that you’re sending data out, to who knows where, via 3rd party sharing apps. That could be a problem. Investigate this. Rein it in.
Architect Your Data
9) What data connections and APIs exist?
You know your teams can automate many of your data streams in various ways. What are you doing and how does it impact data flows?
10) Why aren’t we aggregating data silos to gain a holistic perspective?
It’s a classic problem. Silos of data. (Sigh.) Pretty much one for each tech in your stack. Are you confident in your multi-platform data architecture?
11) Who is responsible for data quality, accuracy, and governance?
These will absolutely be different teams, but knowing who owns what is essential. Search Discovery advocates for specialized expertise and a cross-functional governance council.
12) Where does data flow from one platform to another?
You must know how the plumbing works. Or, if you don’t want to know, then sub it out, but it still has to be managed and maintained. Integrations break down. They need to be monitored with some good old QA.
13) How do we establish metrics, KPIs, and targets that matter?
All too many people think they have KPIs, but they don’t. With good intentions, they diligently watch the minor fluctuations of their metrics. But action isn’t instigated, and therefore, change doesn’t occur. Targets create a sense of urgency. Use them.
14) When does data refresh and update in systems and platforms?
How real is real-time? I’ve asked this question for a long time. The answer depends on how fast you need it. Don’t crow for real-time data unless you’ll use it to dynamically trigger events or optimize on the fly. Otherwise, settle down. Fifteen minute refresh rates should be more than enough.
Activate Your Data
15) What communication methods do we have for sharing data?
We must talk about this stuff. Even if it’s just a 10 minute stand-up to review metrics and discuss targets. That’s optimizing in flight. But we also need the weekly and monthly reports with analysis and context.
16) Why don’t we use data to optimize assets in near real-time?
This is pretty much the crux between looking backwards and being reactive, or looking forward and being proactive. Being in the moment—and at least a half step ahead—is where it’s at.
17) Who in our company uses data to model predictive decision-making?
So we agree you need it, good…now who’s gonna do it? Don’t place all your hopes and dreams on a single data rock star. Data management, modeling, and analysis requires inputs from around the business.
18) Where can data trigger automated actions (e.g., content, email, SMS?)
Activation means doing something, so here’s your chance. Use data to know when to reach out to customers—and on which of their devices—and do so in a consensual way that builds trust and puts the customer first.
19) How can we use data to accelerate our digital transformation?
This is a cultural shift. It will take time. Prove the value by illustrating what forward-thinking looks like. Whether anticipating the perfect audience, or configuring the algorithm two steps ahead, your transformed future awaits.
20) When should we revisit our data strategy?
Annually, at minimum. Today, if you’re worried. Tomorrow, if you’re a confident overachiever. But sometime soon, so that you can document and defend why you’re activating data with controlled abandon!
So, there you have it…The questions you should be asking of your data strategy. Oh, but I did promise you twenty-one questions, didn’t I? Your last question…
21) Need help building out your data strategy?
Drop me a line. I’d love to chat. A data strategy is a terrible thing to waste.
And, of course, no single blog post can address all of the questions about data strategy, so if you have ideas or additional questions that I forgot, please reach out with the form below, tweet me @johnlovett, or reach out using the form below. I’d love to hear your thoughts. Happy strategizing!
