This year was my first time attending Digital Analytics Hub. It is unlike any other I’ve been to. Gone is the all-too-common format of sitting in a big room with someone speaking to you over a projected power point. The only time this did happen was a provocative opening keynote that looked to establish a new way of communicating insights to stakeholders. I definitely welcomed it, and if you’re an analyst that finds yourself building decks to tell a story around insights, I highly recommend you check out Lea Pica and her method at www.leapica.com.
So instead of one-way lectures, you go to huddle rooms of about 10 – 20 people and discuss a topic picked by the leader of the huddle. Before you get to the conference, there is a list of topics that you can go through and pick the ones you are most interested in. Since the point is to get people talking, there’s only a limited number of slots to preserve small huddles, so conference organizers Matthias Bettag and Michael Feiner invest a good amount of planning and logistics to make sure attendees get to attend the huddle they prioritized higher while selecting them. You can read more about how it all works by going to the Digital Analytics Hub site
I think the audience for this conference is also different from what you would expect on other conferences. I noticed a good mix of client-side attendants on the top of the ladder, titans of consulting, and vendors at the cutting edge of the industry. While you can find this level of attendants in other conferences, the density here is high, and it makes for very interesting discussions.
There’s also a hard rule you must not break. What is spoken in the huddle rooms, stays in the huddle rooms; or how someone called it “The Vegas Rule”. With this format, the conference lends itself for attendees to open up about their experiences. Couple that with attendees that know what they are talking about, and you can get a mix of well-thought arguments, and sometimes even controversial back and forth! It would be interesting to lead a huddle one day with a controversial topic of my own…
And now, without breaking my Vegas Rule vow, I wanted to share some of the discussion points I found most interesting, as well as my take on the sessions I attended:
Data Analyst vs. Data Scientist
- Is there a clear-cut definition of a Data Scientist? Is that line drawn at IT skills? What about statistical skill? What are different Data Science roles? Is there title inflation in the industry? How do analysts and scientists interact and/or augment each other? What to look for when finding either an analyst or a scientist?
- What I personally took out of this most was there’s a lot of work that should go before hiring a data scientist, such as investing time in defining the problem a scientist is hired for. This helps clarify whether the organization needs a scientist or analyst.
Death of Data Analyst/Scientist?
- Will we get to a point of technology vendors offering data science as a service? Are we already there? Will the industry evolve to see laymen using data science; is that good/bad? Should non-scientists use data science and what is a responsible level of skill required to employ data science? Are data scientists ‘sciencing’ their way out of a job? Is there art in analytics, and can either analysts or scientists bring that art to the table?
- It was interesting to see the differing opinions on the level of technical acumen that either analysts or scientists people think should have. Also thinking about how technology is lowering the barrier of entry into ‘data science’, is that a good thing or a bad thing…
Leveraging Digital Data to Deliver Laser Targeted Reach
- Should organizations buy into established digital experience technology stacks? When should organizations build in-house systems? What are potential pitfalls in going down this path, and how could we avoid them? What are some valuable personalization tactics that drove ROI?
- There were many aspects to successfully target people based on data. My take on this huddle was to outline a rough “draft process”:
- Define use cases; which in turn defines the strategy and toolset. Without this you can risk building with no purpose
- Determine budget; do you go with an established technology stack, or do you build in-house? Who are your partners?
- Determine data collection sources and storage; 1st, 2nd, and 3rd party data, data onboarding partners?
- Determine the taxonomy of your data; implementation strategy which in turn drives segmentation
- Experiment with data activation tactics; test out what drives most value
- Optimize with a closed feedback loop
Data Analyst in a world of Digital Transformation
- What does Digital Transformation and being Customer Centric really mean? Are these just buzz words that executives bandwagon onto, or are they essential for companies to step into the present and remain relevant in the future? Is digital transformation a cultural, technological, political, or organizational change? Is it all of them? Is a technology startup organization already “digitally transformed” by default, or is this a continuous process?
- My favorite part of this session was the group possibly coining the new term “Digital Iteration”, or as someone wittily put it, “Digiteration!”
- As owners of digital analytics solutions in organizations, should we expect stakeholders to have analytics know-how in today’s digital workforce? How do you rate your analytics solution is adopted and being used for its purpose? Is there a hard-set staffing rule of X analysts per stakeholder? Is analytics a support group or peer group to the business?
- It was interesting to see how prevalent the challenge of analytics adoption is across organizations. There were many tips shared on how to help drive this, and some new point of views I had not though about before.
Mend the Skills Gap
- What types of roles are available to analysts? What are the skills needed? How do you train them? Is it better to start as an implementer specialist, or as a reporting specialist? For that matter, what is an implementer? Are implementers solution architects? Does the word ‘analyst’ in a job title turn people off? What is the best background for an analyst, for either reporting or implementation?
- Unfortunately, I had to leave early from this one, but there were some good tips on what to look for in prospective new hires.
All in all, this was an excellent conference, and don’t think the experience can be replicated by others. I hope to attend next year’s in Virginia, or at very least have someone from Softcrylic attend. So if you end up going, see you there!