Data is one of the four main pillars of any great digital program along with people, process and technology. Because there is so much data that lives across an organization—and everyone has some use of it–data needs to be treated like a common asset much like water or land (have you ever heard the term “data lake”?) and managed to the common good.

This article explores the management of data through a political lens. Not Republican vs Democrat type of politics but looking at the realities of the human side of data management and what that means to individuals, teams, organizations and culture. My objective here is to help you improve the success rate of working across teams to increase the enterprise value of data.

Some key themes we’ll explore:

Ownership and control
Shaping the narrative
How data management reveals the true culture of an organization
Governance is not just for governments
Small town vs big enterprise data management
How to approach those that control the data

Data Ownership and Control

We’ve all heard the phrase “possession is 9/10’s of the law”. This translates directly to both politics (one of the primary functions of politics is to create laws to guide our society) and data (in that who “owns” specific data is the primary constituency of use of that data). While the greater organization officially “owns” the data, the team that generates, manages and uses the data feels intense ownership of that data and the usage that data outside of that group.

Like water, data flows through an organization and the social web map of who controls the data continually evolves as the teams, managers, owners and tech stack changes. This is not unlike political districting maps evolving as political leaders change and populations migrate from rural to urban environments.

For example, if your team is responsible for managing the systems that generate security alert data, there is an organizational imperative to ensure that these alerts are distributed properly, received, acknowledged and acted upon. The social web of this data is extensive and can be a primary imperative to protect the security of systems and data due to the high risk of a breach.

From a political perspective, it’s important for the team that wants to access the data and use it for a specific purpose like enhancements, communication, analytics or reporting to be sensitive to the group that feels the most ownership of the data. Invariably, the data will need to be moved or modified in some way to make it more broadly useful and this requires communication and consent.

The more that a team can work with the “owners” of the data and provide regular feedback regarding the modifications and benefit of the use of that data – the more likely you are to get “votes” or buy-in to support the continued extension of the value of the data. In the security alert example above, the data might need additional context or status in addition to just the counts of the alerts.

Shaping the Narrative

It’s easy to think of data as a cut and dry set of numbers. But, in reality – just like in politics – it’s all about the narrative we construct around the data. That is where the real political value is.

For example: is 1,000 website visitors good or bad? How is a website visitor defined? What is the time frame of the 1,000 visitors and what did those website visitors do? Is 1,000 visitors more or less than the previous time period? What about last 12 time periods? The context of the data point in relation to time, impact, business value and other metrics helps define the narrative around the data.

Compare this to the narrative that a politician might construct around an issue. How is the issue positioned, for/against some other idea, closer or farther away from a desired outcome, to encourage or suppress constituent involvement? When presenting data internally, the person who is telling the story and controlling the narrative has a lot of leeway in how the data is presented (think: graphs that can be modified to tell two stories with the same data), what is emphasized, what is included or NOT included and who is exposed to not only the data but the story behind the data. Dashboards, presentations, daily metric emails or Slack notifications can all present data, but without the context and story behind it – they are just data points.

Anyone who wants to be an effective influencer of the acquisition and use of data should take the time to learn how to tell stories with data. There are many excellent resources and training programs to gain this skill, but approaching each data deliverable as an opportunity to tell a story is the right mindset and perspective.

How Data Management Reveals the True Culture of an Organization

Let’s start with a real-life example. A software engineer is making improvements to a data system and she finds a mistake in how a key metric was being stored or calculated or interpreted. The error is not insignificant and will change the narrative of the particular program and impact reports and dashboards to the broader organization.

In this case, is the software engineer treated with respect because she discovered and is able to fix the error or is she treated like a whistleblower in an organizational structure that is more concerned with CYA (cover your ass) than transparency and improvement? What about the software engineer who originally made the mis-calculation? Is that person still in the organization? What about their manager or the person ultimately responsible for that program and data set?

How an organization handles a situation like this is a direct reflection of the culture. A culture with a growth mindset built around learning, improving and progress is going to be more open to understanding why the error happened and what needs to change so that future mistakes are minimized. An organization with a fixed mindset that is more command and control, fear of change and intolerant of mistakes will place blame and reward status-quo leading to restricted improvement and growth.

Which type of organization are you in? The leaders of the organization drive this culture through communications, vision and emphasis. Beyond the words of what leaders say (either political or organizational), what are their actions and do they reflect their beliefs and promises? Actions speak louder than words in both politics and business organizations. Take action today to uphold or change your culture in the name of transparency and better data management for all.

Governance is Not Just for Governments

Governance is a term that I, personally, have struggled with over the years. It sounds so… bureaucratic. But with data, and particularly systems, it’s an important aspect of keeping information about customers, the business, products and employees secure. At its core, data governance should make it easier for the organization to store, secure, and use quantitative and qualitative information.

Key elements of data governance align closely to how layers of local to national governments manage the information that we entrust to them: social security numbers, work and tax information, census data, birth certificates and residency location:

Accessibility – Who has access to what data is important. Permissions management is just one piece, but more broadly it’s a similar conversation around who can vote or not in the current political climate.
Availability – Where is data available and in what format. Aside from permissions, is more useful data available to more people? And with the right governing rules to use for improving customer experience and the quality of life for citizens?
Quality – ad data is worse than no data. Energy must be put into ensuring quality data enters systems (not unlike the 10-year census data cycle) and continually cleaning/validating data so decisions are based on good data.
Consistency – The more consistent the definitions of data and how intake and management is across the organization, the easier it is to use at the enterprise level. Think of all of the different organizations in the government that deal with safety, security and intelligence and the stories around how they haven’t shared data to identify threats or when they try and share – that is unaligned data .
Auditability – The ability to validate data and make it transparent. That data is good and being handled correctly is an imperative to both businesses and governmental agencies to ensure that data is being handled in ways that meet the promises made to customers, investors, voters and citizens.
Security – Protecting data from foreign or internal threats or honest misuse is an imperative that sits on the organizational/governmental structure around the data.

Small Town vs Big Enterprise Data Management

In a small organization with fewer tools, teams and technologies, data politics are simpler. There are fewer layers of management, fewer layers of permissions and approvals and fewer people that need to be convinced of the recommended use of the data. Think about a small town where you know the mayor because her kid plays baseball with your kid. You can simply have a conversation with the mayor at the game and get her attention on a topic. The distance between your need and the person who has the power to make decisions is shorter and the “politics” are simpler.

It’s important to note that a small organization may not mean small data since the ability to find, acquire and grow data sets is incredibly easy today. However, a larger organization means more people, more siloed data, and more functions that have a specific need for specific tools that drive specific outcomes.

As the size and complexity of an organization increases, the need for a relationship/political approach increases as well because the separation and uses of data becomes more complex.

More data, more systems, more people, more groups, more laws, more governance, more responsibility.
Interactions and hand-offs between groups are more challenging and need to be more clear.
Agreements around data quality, how fast issues get resolved and speed of delivery are all important and move into a contract type status (Service Level Agreements – SLA’s are useful).

How to Approach Those that Control the Data that You Need

Here are some considerations filtered through a personal/political lens when you realize there is data that you need that you don’t control.

Is there an individual benefit approach? Start with the classic framework of WIIFM? (What’s in it for me?). By positioning the ask for access to data, share what the team with the data can get out of it. Beginning with this perspective gets directly to the “all politics is local” sentiment.
Is there a social consensus or leadership opportunity? The next level of appeal is thinking about the greater social impact or in the case of an enterprise – how does this data work impact the organization in a positive way that will make everyone’s life better.
Is there a toil sharing approach? The management of data might be a significant burden to the team you are working with so there might be an opportunity to share the management or cost load in some way. Engineering teams, including data management, are always looking for ways increase efficiency and decrease toil. If you can tap into this, you’ll have a more eager audience.
Is there a relationship approach? Politics is also personal and built on relationships. Curating relationships over time is as critical within in a business as it is within a political organization. Either using this data interaction as a way to start a relationship or build on an established organization can short-cut red-tape and process if required.
Is there a power structure approach? This is really a last resort, but there may be a need to play the power hierarchy card. Getting someone’s boss’s boss to make this data project a priority can give you the cover you need to make progress. Governmental and military establishments are all about who reports to who and what the leadership does from the top down. Ideally, everyone is on-board but issues around budget, human resource allocation and strategic intent all play into cross-team sharing of data.

Closing Thoughts

The management of data is both extremely important in our era of technology revolution. It is also a lot of work to acquire, store, manage, augment, maintain and share. There is so much dependence on data to enable the vision of different functions throughout the organization so access and usage is a critical enterprise level skill. The organizations that share data more transparently or as some say, “democratize data” (another political reference) make more informed decisions, learn more rapidly and grow more quickly.

It is both a competitive advantage and an organizational imperative to build-in the people side of data management because as we know – when you have people, you have politics.

Finally, for those of you who have made it to the end of this missive, yes – the title is a riff on “The Politics of Dancing” by the one hit wonder Re-Flex from 1983. Try to get that earworm out of your head. Enjoy.

I recently published this article on MarketingProfs.com here. Enjoy!

I’m officially sick of the term “Big Data”. Marketers have access to lots of data, got it. Big Data in and of itself doesn’t sell more products/services or make high value prospects aware of your brand. Big Data is merely the pool in which we swim to target and generate results. Smart data is the spear gun – this is where we need to focus.

As direct response marketers, we think of data in terms of insight to make better decisions that drives to an action. Smart data breaks into the following categories:

1) Creative Optimization – Every one of our creative concepts comes from an insight from customer research and/or campaign data. Based on some form of insight around what a particular target audience is doing, thinking, feeling, responding to, NOT responding to or is concerned about – we build a creative approach to elicit a response.
We start many of our creative exercises with the search for a “wow, I can’t believe that” type number. For example, one brand we worked with targeted to children, an inspiration came when we learned that 97% of kids play videogames regularly, but only 33% of kids get regular exercise. A body at rest tends to stay at rest, but a body in motion tends to stay in motion. That meant they needed to inspire kids to turn off the games, get up and get going.

The stats that inspired this brand’s communication have nothing to do with Big Data – just smart data.

2) Campaign Optimization – This is where the market dollars meet the proverbial road. Using specific data to plan a campaign, target a specific audience, optimize media against what is working/not-working and testing variants like message/offer/call-to-action/creative theme/etc. is extremely data centric and requires a smart approach.
For example, it’s possible to optimize a DRTV campaign on the fly. During an 8-week flight, we’ll see which markets convert at a higher rate during the initial weeks and focus our media spend there. We can also use response data in our digital buys to leverage the ad serving technology to optimize campaigns in real time. Pre-planning the use of data and optimization is critical in campaign optimization.

3) Conversion Optimization – Conversion optimization is a unique data set in and of itself. Sources of traffic are an input but the data focus here is getting the customer to take the next logical step using micro actions, calls to action, call center optimization, website optimization. There is a heavy reliance on split testing and multi-variant testing that requires smart test planning and execution.
In a mobile marketing campaign, for example, does a click-to-call convert at a higher rate than a get-more-info button? Should the button be red or orange? Which headline is converting better on a desktop vs tablet vs mobile device?

4) Campaign Reporting – Success (or not) of campaigns and programs can be shown in reports that are as detailed as needed for analysis or rolled-up into top level metrics for a helpful overview. Smart reporting of data helps tell the story of progress, what the ROI of the campaigns are and what the marketing dollars of the organization are contributing to key performance indicators (e.g., sales!) and whether these numbers are improving or sinking.

Having a history of results can help you avoid mistakes like reading the data too soon. If a campaign hasn’t had a chance to mature, you may pull the plug too early.

5) Attribution Analysis – Attribution analysis is a difficult thing to do well when you start layering in multiple campaign sources, offline and online, multiple sales channels and different length of buy cycle. We separate out campaign analysis from attribution analysis because they serve two different purposes:

Campaign analysis and optimization is more tactical. It answers the question: How can I get better results out of the marketing dollars I’m spending on this campaign/channel?
Attribution is more of a budgeting and allocation process. It helps to answer the question – based on best available data – where should I be allocating MORE or LESS marketing dollars over the next cycle?

6) Executive Reporting – Selecting the right metrics to surface to Directors, VPs and C level types is a skill unto itself. How this data is presented, how often it gets updated, what charts/graphs/visualizations are used and, especially, what story the data is telling is extremely important for communicating progress of your programs as well as outlining opportunities for what’s next.

This kind of reporting is not something you set and forget. It needs to be carefully crafted to avoid any kind of miscommunication. In fact, I recommend you set a regular appointment and present the data in person, so you can tell the story that you want to tell and personally address questions, concerns or issues that come up.

7) Privacy and Compliance – as an ISO27001-certified company, we rely on systems we put in place to process and handle data to comply with the strict requirements for remaining certified. This includes how we separate client data, keep healthcare Information, Personally Identifiable Information and digital behavior information separate from each other.

I’ll take quality data over quantity of data any day of the week. Understanding where the data is coming from, how it’s stored, what it tells you will help tremendously in how you use it to narrow down to the bits that allows smarter business decisions based on the data.

The Politics of Data