Data tells a story, because of this it can also tell propaganda.
Gaming the system: statistics can be faked as noise can become signal through bogus selective-picking, and sampling.
Data shouldn’t be the last chapter in your story. Otherwise, you risk utilizing data to just support what has already happened (the good and the bad)Many clients use the end of the quarter to crunch all the data and re-enforce a narrative with senior management. Because of the artfulness of the reporting, this type of process takes up a significant amount time and energy from departmental senior management. This is just a flat out waste of resources - as an old NBC colleague of mine said: "Imagine how well rested and productive we'd all be if they just gave us the time off rather than spending 3 weeks a quarter developing the quarterly report". Which by the way, wasn't an exaggeration.The philosophical shift from that to using data as a form of guidance is a monumental one and shouldn't be underplayed.
Time to repent.
More data, shouldn’t equal more problems.
Turning data into information.
LHF low haning fruit, system with like strcutres or with some structure“Smart Data” means information that actually makes sense. It is the difference between seeing a long list of numbers referring to weekly sales vs. identifying the peaks and troughs in sales volume over time.Smart data is data from which signals and patterns have been extracted by intelligent algorithms. Collecting large amounts of statistics and numbers bring little benefit if there is no layer of added intelligence.
DataWeek: Oh no, I'm running a data-driven cult!
October 2, 2013 DataWeek: Oh
no, I’m running a data-driven cult! hugeinc.com firstname.lastname@example.org 45 Main St. #220 Brooklyn, NY 11201 +1 718 625 4843
Oh no, I’m running a
data-driven cult. Leala Abbott Senior Content Strategist Huge Inc. Leala Abbott is a Senior Content Strategist at Huge, helping to shape, define and interpret metadata and taxonomical needs into content structures everyday. She holds a Masters Degree in Information Science from Rutgers University.
“Story and Data need each
other. Data without Story is Mythology. Story without Data is Propaganda.” - Huge / Content Strategy Motto
Data as propaganda. The big
data method. The scientific method. hunches refine hypothesis hypothesis Gaming the system results test results test
What story are you telling?
Susceptive to fallacy. Many clients use the end of the quarter to crunch all the data and re-enforce a narrative with senior management. Open to truth. • Put data in the passenger seat (not the drivers seat or the trunk). • Use data to guide, not just narrate. • Reporting needs to be ongoing, fluid, and light. • Leave the artfulness of the quarterly report behind.
The question you have to
ask yourself is if data tells you something that goes against the current gospel, do you share these truths?
The path. 1 Have a
goal. 2 Give instruction. 4 Ask technology. Lack of the proper filtering mechanisms, both from and human and tech standpoint to abstract the signal from the nose. The frequent metaphor people use "is drowning in data”. Big data wont magically solve your problems, you’ve got to have a clear objective. 3 Balance cost / accuracy. Everyone talks about the role of 3rd party data targeting, personalization and optimization but not all data is created equal. We need to do a better job balancing the cost of the data with actual accuracy it provides. They aren’t drowning in data, they are waving for help. People need guidance and proper insight into interpreting the data. Some of the concepts used in attribution are so technical and arcane that the marketing lead simply doesn't know the right questions to ask. Data is being created at breakneck pace. So correlating, interpreting and analyzing it needs to happen just as quickly. This is where the technology comes in as humans can’t do it fast enough, or older systems just aren’t structured to do it efficiently. You need flexibility and innovation.
Start converting. Establishing new goals.
Your approach to data should grow and change as you do. Find inefficiencies. Your architecture (and your org) should support the efficient filtering of data. Create efficiencies. Bring structure to your data or provide an interpreter. Business change Establish new goals, new teams, and more refined and efficient data, to deliver the real truths to your organization.
Turning data into information. Planning.
Structure. T argeting. Put together a strategic plan, which can be used as a guideline for your approach to working with data. Audit to find LHF. Whenever feasible give the data deeper, semantic meaning through structures prior to it being collected. Don’t try and take it all in at once, target specific data sets the meet primary goals, and objectives first to start modeling. Prioritization and measurement. Consolidation and aggregation. Integration. Gather and house the data in a centralized repository. Don’t have customers emails across 4 databases. Enable the data to be integrated into business systems and decision making processes. End the ownership fighting. Prioritize within your targeted data sets and determine LOE. Start small. Is it measurable is it valuable?