How Does Your Data Treat its Neighbors?

Does your data live nicely with its neighbors? How does your data treat its neighbors? How is your data treated by them, and do they work well together? Or are they separated by big walls? Living in silos? Perhaps your data system seems to constantly fighting with data systems used by others in your field? Your own multiple internal systems could be feuding, leaving a mess all over the place. Then again, your data system/s might feel little and insignificant against the new big kids on the block: “Big Data” and Blockchain Data. It might also be true that your data could feel old, neglected and lonely.

Let’s parse this.

1. How Data Can Behave and Live Nicely with its Neighbors.

Data systems have been confusing for many people. However, even when a team really understands the elements of a system, managing all data requirements can be downright taxing for most of us at times. Managing includes staying on top of hardware and software requirements, staff training and updates, and using data for quality improvement, evaluation, planning, and reporting. Sometimes, the fault for data misbehaving “lies not in our stars, but in ourselves.” We might lose track of an upgrade or a change, or get caught in a too-tight deadline when mistakes happen. However, data itself can misbehave, and finding the gremlins that cause the glitches is often a troubleshooting conundrum.

It may well be that the biggest cause for some data not living nicely with neighbors comes from incompatible systems and closed architecture systems. In one area of the Southwest, there have been over 300 data systems being used by different providers. Many of them are incompatible with one another. It’s as if each data system were a neighbor with very high walls, or living on an island. Crosswalks have to be created to link these incompatible systems. Or, in many cases, agencies simply have staff re-enter data into the multiple systems.

We need to create policies that facilitate system interoperability and compatibility, and reward those systems that are compatible and easily interfaced.

2. How Data Systems Can Stop Fighting with Others

The competition among many different data systems is fierce. Over the years, there has been some winnowing, and most fields have a few very large data systems that are becoming standard. However, there are data providers continually developing new data systems and networks which they’re selling to local, state and federal governments for managing a wide range of activities. Sometimes these systems do not deliver the effortless romp through the data neighborhoods they promise.

This leaves providers and government agencies struggling to manage both the policy and implementation challenges, while living with a large expenditure of funds that has not quite delivered what was needed.

Some of these difficulties are part of the developmental cycle, as governments and agencies move from initial IT system installation and development over just the past twenty-some years, to more complex data systems. The IT system design potential is often greater than capacity to successfully implement – often for system developers, purchasers, and end users. One of the ways these problems can be minimized is for government agencies to (a) research current system strengths, weaknesses and needs; (b) system options available; and (c) develop system protocols that are based upon well defined key elements and parameters. Then, (d) the new or revised system should be thoroughly beta tested with a small pilot group of savvy providers; (e) end users should be continuously included in feedback and discussions. Finally, it might not hurt to (f) build in performance-based payments for successful system implementation, and fines for glitches that are not addressed.

3. Managing Gaps: Those That Feel Insignificant Against “Big Data” and Blockchain Data

In many communities, small and mid-sized agencies have been working hard to develop more data-driven services. However, many of them are still juggling a good number of separate data systems that require either crosswalks, or entering data multiple times. It’s difficult and expensive to build integrated data systems. Some agencies report they are trying to see if the story they hear is really true, that those large agencies moving fast ahead with their data systems are thriving because they found the magic elixir.

Unfortunately, there is no elixir, and if there were, it would be on the market for a very high price. The “magic,” as it were, comes from integrated systems built slowly, carefully, with many iterations, and at great cost. Because so many elements of data are being set by a range of different funding sources and accreditation or certification agencies, creating the integration is an ongoing challenge. Those agencies that are large, that can spread costs over many programs, are much more able to build these integrated data systems.

Now that “big data” and blockchain data are entering the scene, they are like the new, very wealthy neighbors who just bought land nearby, and are now building huge, expensive homes that seem to overshadow just about everything in the neighborhood. These expensive additions to the neighborhood cause some people who live nearby to feel rather insignificant compared to their wealthy neighbors. They worry about how they can compete with these new neighbors, and how they can manage the new, higher property taxes.

It may well be that the development cycles for creating new, more comprehensive big data systems are moving much more quickly than the development cycles for successful data implementation – especially for small and mid-sized agencies. This could create increasing challenges in the future. Or, after some time, it may simplify data management and reduce costs. Time will tell.

4. Addressing Lonely, Neglected Data that is Rapidly Aging

Some data may feel like the neglected neighbor, lonely, isolated and rapidly aging. Every data system needs to be integral to the work it supports, part of ongoing quality improvement, and used and updated on a regular basis. If data is not an integral part of the operation, ongoing quality improvement suffers, and the potential power of the data and the information system is limited. In our busy lives, we often don’t visit with our neighbors as much as we’d like, or as much as we did decades ago. The same is true for many staffers who are not born or dyed-in-the-wool data geeks. So, reach out and touch both your community neighbor, and your staff.

Part of the challenge that agencies face is the real cost for maintaining a responsive, integrated data system that is seen as integral to mission, and essential to quality programming. The real cost includes staff training and development time, building of a data-driven culture, regular large outlays for hardware and software, as well as ongoing system development costs. Unfortunately, when shortfalls occur, information systems and capital improvements are often cut when agencies face extremely difficult, often impossible budget adjustments.

Summary

This data storytelling is meant to provide living and realistic neighborhood metaphors for some data key issues, which I hope are helpful. I am a researcher and data geek who loves data, and a health systems consultant concerned about the size and scope of the challenge. There are both major and minor issues faced by policymakers, system funders, certification agencies, IT developers, software companies, governments, and end users. If that seems like a large list, it is. The data challenges and opportunities represent pieces of what is one of the most significant changes in our lifetimes. Data is a major component of our current IT revolution, which is as big as (or bigger than) the industrial revolution was for much of the world, over 100 years ago.

Many government agencies at all levels are helping to shape this revolution through policy, funding and contracting. To facilitate managing this fast-paced change, government leaders can look at models and effective practices of their peers. Sharing models and effective practices in within government associations and networks, and between government agencies and others is a fast-growing practice that can help us manage many of these challenges. The sharing provides platforms for exciting dialogue, faster learning, and practice integration.

Providers  on the frontline, leading successful IT implementation can share their knowledge learned from the successes and pitfalls with other agencies, to help speed up the learning in the field. Many agencies will need to budget larger amounts of funding for hardware and software upgrades, data integration, staff training and development, and ongoing system development and maintenance. Budgeting more for IT in a funding environment that includes more cuts in funding for nonprofits, and greater competition for resources, is difficult. However, those agencies that are able to develop data-driven programs and services will emerge as increasingly competitive, stronger and more sustainable for the future.

I’m interested to hear what governments and agencies are doing to facilitate data neighborhoods that are more integrated, friendlier, and more effective. Please share your stories with me at: aegan@cybermesa.com.

  1. Footnote: A twist on a quote from Shakespear’s Julius Ceaser (I, ii, 140-141), where the speaker, Cassius says “The fault, dear Brutus, lies not in our stars, but in ourselves, that we are underlings.”.

Anne Hays Egan
New Ventures Consulting
All Rights Reserved

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