--- title: Out with the Old, In with the New? description: in, out, technology, project, data, governance, intelligence image: https://blog.idatainc.com/hubfs/StockSnap_FQCCUDQ3N0_blackboard_OutOldInNew_BP.jpg --- [![iData_notag_tm](https://blog.idatainc.com/hs-fs/hubfs/iData_notag_tm.png?width=184&name=iData_notag_tm.png)](http://www.idatainc.com) # Out with the Old, In with the New? ## Out with the Old, In with the New? [July 12, 2023](https://blog.idatainc.com/archive/2023/07) By  [Aaron Walker](https://blog.idatainc.com/author/aaron-walker) In [technology](https://blog.idatainc.com/tag/technology), [in](https://blog.idatainc.com/tag/in), [project](https://blog.idatainc.com/tag/project), [out](https://blog.idatainc.com/tag/out), [software](https://blog.idatainc.com/tag/software) ![StockSnap_FQCCUDQ3N0_blackboard_OutOldInNew_BP](https://blog.idatainc.com/hs-fs/hubfs/StockSnap_FQCCUDQ3N0_blackboard_OutOldInNew_BP.jpg?width=370&height=246&name=StockSnap_FQCCUDQ3N0_blackboard_OutOldInNew_BP.jpg)The big day last week in the United States was Independence Day, July 4th, and we hope everyone was able to enjoy it in their own way. The first week of July is often significant and occasionally momentous for many of our clients because it represents the end of one fiscal year and the beginning of another, with all that entails: new budgets, new expectations about spending and revenues, and frequently new technologies. July 1 is also a frequent “go-live” date for new software and data applications. With that in mind, we expect some of our clients didn't get much of a holiday last week. But it gave us a chance to reflect a little on what we've seen over the years. Targets and go-live dates help focus the mind, they give direction and meaningful parameters to projects, and, in the situations we’re thinking of, they also represent a certain kind of clean slate. From this day forward, we’re going to record events this way, instead of the old way; we’re going to have a new and better way to access and share information; we will be able to process and organize events more quickly, or more clearly, or more…something. There is always a temptation to believe that simply because something is new, it will be better.  And yet it turns out that it’s hard to get a completely clean slate. Many of the organizations we work with take days or even weeks to “close the books” for a fiscal year, as they wait for end-of-year purchases to finalize or for other financial transactions to complete. Sometimes it’s policy, and sometimes it’s exigency (urgent), but we’ve seen situations where new expenses are embargoed for some time while liquidity is secured. So units may technically have funds available to spend, but no approval to make any expenditures for some period of time. Budgets frequently undergo daily revision at this time, and so it’s not uncommon for recurring or pre-approved expenses to go through but for new encumbrances to be delayed (or for the unofficial position to be, “hold off as long as you can”). In the face of uncertainty, it may be safer and wiser to be prudent if not penurious (stingy). Careful planning can of course allow you to take a few weeks or even a month off from making purchases, but this practice does rather disrupt ongoing operations. The expression, "clean slate," derives from the classroom practice of wiping chalk off a blackboard, we think. (We're not etymologists and we don't have that much investment in this colloquialism!) Some of us are old enough to remember doing this, although in fairness wiping down a whiteboard isn't all that different. But as we remember, if you weren't careful, or if the chalkboard was old enough, the palimpsest (writing material) of previous equations or spelling exercises or historical dates could still be visible. If you were really sloppy, you might even find yourself in a situation where the old notes and the new work were both visible, and to someone walking in the room that could be confusing or disorienting. And this seems like a pretty good metaphor for what happens in business! People who work in finance and accounting are well familiar with making adjustments, creating or modifying journal entries, and so on, and so they know transitions across fiscal periods can be a little messy. They have practices to make clear what's going on, and in most cases some lived experience with these transitions; a person who doesn't have a financial background might well be put off when they expect clean ledgers on July 1. On the data side, clean slates are similarly unlikely, even when you’re starting with new software applications. If your new software is a replacement for some old software, you’ve probably got to migrate data or business rules or user privileges, or all three (and more!), from the old application. There’s always a chance that the migration wasn’t perfect, for any number of reasons, and so it might make sense to tread lightly, so to speak, once you’ve rolled out the new tool. We generally see one or both of the following techniques: dual entry or side-by-side processing, where you duplicate the work for some period of time until you’re certain that the new system works at least as well as the old one; and closer than usual scrutiny of the outputs of the new tool, to confirm that the journey from data capture to data release, in whatever form, is at least as reliable as in the old setup.  “At least as reliable” is not much of a baseline, but there are too many stories out there in which the new tools don’t work as well as the old ones, which already didn’t work well enough, for them all to be apocryphal (false) ! In addition to recognizing real concerns such as employee burnout, we’re always additionally saddened by dual processing, or frantic backfill around operating reports and management dashboards, because it often would have been a lot less work to do the data governance activities to ensure preparedness than it ends up being to do remediation! Defining data terminology, developing data quality standards, and fully understanding the data lifecycle pay special dividends when you’ve got new tools with which to manage your data. Now, if the new software is designed to provide brand-new functionality, you might be working with something approaching a clean slate. At least there’s probably new data being collected, or data being combined in a new way, or some other aspect for which there is no previous standard for comparison. Still, that new functionality probably has to fit in with existing business processes, and it may have to be mastered by existing employees who have internalized how things work at their organization. If there’s a history of cobbled-together workarounds and shortcuts at an organization, whether in financial matters or data practices, that history tends to apply smudges to any clean slate projects or applications. Most of our clients have done exhaustive due diligence before buying new products like these, so it’s always a bit surprising how quickly what these tools actually provide can diverge from expectations. This isn’t always a bad surprise: sometimes expectations were so low that almost immediately the new product exceeds those expectations. But we often see clients settling for what amounts to pennies on the dollar, having invested resources in products that provide only marginally more return than the products they’ve replaced. Whether legacy technology is being replaced or upgraded, or new tools for new needs and initiatives are being purchased, we think the challenges of really turning over a new leaf have similar roots. We speculate that in addition to the power of organizational history and culture, one or more of the following might be at play. 1. *The initial diagnosis was faulty*. For example, data quality may be perceived to be so poor that we cannot generate reliable reports, so we’re going to handle the cleansing by forcing it to follow rules in the data warehouse. But sometimes it turns out that the data quality was just fine, it was just the data was poorly understood. So the data warehouse cost a lot of time and money to build, and it now generates more reliable data, but the value of the output is much the same. 1. *The product solution cannot or does not address the underlying issues*. The diagnosis may have been correct: we’re not using data well enough to inform or support our strategies or operations. But if the cause of that problem isn’t the usability of analytics tools, but is rather a fundamental lack of trust in the data (or analytics generally), then replacing the BI stack and making more data available might be a net benefit, but it doesn’t do anything in and of itself to create that trust. 1. *The objectives weren’t clearly understood or explained*. Maybe we want to provide quicker turnaround when customers have issues, so we’re upgrading our CRM, or even starting out with our first one. The CRM might provide more comprehensive visibility into the history of that customer, but does it actually have any features to make our team any more responsive? And did we focus on those features during training and implementation, or did we get seduced into scope creep by all the other bells and whistles of the product? Clients present themselves to us with all manners of data-related issues, challenges, and goals. To deal with these challenges, usually something has to change. Does that change always amount to starting over with a clean slate? Well, it’s always an option, and you could do worse than use the 4th of July, or the beginning of your fiscal year, as a signpost, even if it’s somewhat arbitrary, to evaluate what’s working, what needs to change, and to schedule that change. However: Don’t let shiny new surfaces, or round numbers, or special days and dates substitute for the painstaking work of diagnosing your real data challenges, understanding the cultural as well as technological bases for those challenges, developing meaningful goals and objectives, and picking out solutions that actually help move you towards those goals. Our data intelligence and data governance solution, the [Data Cookbook](http://www.datacookbook.com), helps you get a handle on your data assets, and it is expressly designed to clarify thorny questions of data provenance, usage, responsibility, at every point in its lifecycle. The [Data Cookbook](http://www.datacookbook.com) helps you document where data is used, by whom, and in what ways, and when fully leveraged it can provide valuable insight into whether it’s your technology, your policies, your procedures, or your people that prevent you from meeting your data goals. Remember, a clean slate isn’t the same as a blank slate—and the [Data Cookbook](http://www.datacookbook.com) can be crucial aspect of organizational governance to help understand and act on the difference. For additional resources on major technology projects and data governance check click [here](https://blog.idatainc.com/link-dg-technology-projects) for our resources blog post. Hope you found this blog post useful.  IData has a solution, the [Data Cookbook](http://www.datacookbook.com/tour/?__hstc=129818451.4cbced196dfbfe2941653b4be756cacc.1683659469803.1689194416181.1689198823843.56&__hssc=129818451.1.1689198823843&__hsfp=1261619169), that can aid the employees and the organization in its data governance, data intelligence, data stewardship and data quality initiatives. IData also has experts that can assist with data governance, reporting, integration and other technology services on an as needed basis. Feel free to contact us and let us know how we can assist.  [Contact Us](https://go.idatainc.com/geninterest) (image credit: StockSnap\_FQCCUDQ3N0\_blackboard\_OutOldInNew\_BP #1266) ![Aaron Walker](https://blog.idatainc.com/hubfs/aaron_2.jpg) ##### About the Author Aaron joined IData in 2014 after over 20 years in higher education, including more than 15 years providing analytics and decision support services. Aaron’s role at IData includes establishing data governance, training data stewards, and improving business intelligence solutions. ### Subscribe to Email Updates ### Recent Posts ### Archives - [February 2018 (13)](https://blog.idatainc.com/archive/2018/02) - [May 2019 (6)](https://blog.idatainc.com/archive/2019/05) - [January 2019 (5)](https://blog.idatainc.com/archive/2019/01) - [February 2019 (5)](https://blog.idatainc.com/archive/2019/02) - [October 2019 (5)](https://blog.idatainc.com/archive/2019/10) - [March 2021 (5)](https://blog.idatainc.com/archive/2021/03) - [June 2021 (5)](https://blog.idatainc.com/archive/2021/06) - [July 2021 (5)](https://blog.idatainc.com/archive/2021/07) - [September 2021 (5)](https://blog.idatainc.com/archive/2021/09) - [March 2022 (5)](https://blog.idatainc.com/archive/2022/03) - [October 2018 (4)](https://blog.idatainc.com/archive/2018/10) - [March 2019 (4)](https://blog.idatainc.com/archive/2019/03) - [June 2019 (4)](https://blog.idatainc.com/archive/2019/06) - 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