If you are in the process of spinning up your super cool startup or are a C-level leader at a young organization, you're probably not even thinking whether or not your roadmap is leading you to become a legacy company. Next year you'll be safe from that threat, but if you're lucky, one year will turn into ten before you know it.

Just like your financials, as soon as you collect data and put code into production, then it's an asset worth auditing. In other words, you have to manage future risk. Your data and your digital connection to current and potential clients are valuable, so treat them like money.

Or are they more valuable than money?

A common thing that is said nowadays is that every company is a tech company. We reach customers on social media, we make decisions based on comparative analytics, we present pitch decks with a clear plan to integrate AI with our product. Data is the tool that will get us the resources that we need in the vast tech ecosystem we live in now.

Data, code, and systems auditing is not an original idea of mine, but I have analyzed sloppily compiled spreadsheet "databases" and other systems that have lived for over a decade without standards. As an orderly person (when it comes to numbers), seeing things like this was physically painful. And when it comes to a certain point, it is almost impossible to correct.

Actually, it was impossible because the people who created this mess in the first place were still there making the same mistakes.

In the closing chapter of Martin Kleppman's 2017 book Designing Data-Intensive Applications, he talks about auditability, which obviously appeals to someone like me.

Let's look at some of his key quotes.

"If we cannot fully trust that every individual component of the system will be free from corruption–that every piece of hardware is fault-free and that every piece of software is bug-free–then we must at least periodically check the integrity of our data. If we don't check, we won't find out about corruption until it is too late and it has caused some downstream damage, at which point it will be much harder and more expensive to track down the problem."

"A blind belief in the supremacy of data for making decisions is not only delusional, it is positively dangerous."

"Predictive analytics systems merely extrapolate from the past; if the past is discriminatory, they codify that discrimination."

(See this article recently written by Asmin Ayçe İdil Kaya about AI discrimination in Turkey, which can be easily extrapolated to other countries.)

"With both hardware and software not always living up to the ideal that we would like them to be, it seems that data corruption is inevitable sooner or later."

My emphasis is on the quotes above.

He also talks about the subject of data as surveillance and pollution. It's not great. If you don't already have a system in place for freeing up space, you should develop a plan. A company I worked for paid for web analytics but never used it, leading to an inability to know which features were actually being used.

Do you want to know one thing your competitors aren't doing? Auditing their code and data on a regular basis.

Because the last month of the year is pretty slow due to employees traveling for holidays and enjoying a much-needed rest, your organization probably isn't pushing out big features during this time period. Why not encourage your data and engineering teams to identify and prioritize the issues that have been a pain in their necks?

McKinsey published an eyebrow-raising (and eyeball-rolling) report in August, and since then there's been a lot of talk on the internet about measuring the productivity of engineers. If you want your technical folks to be productive, then giving them a sense of autonomy over their future work, and providing an environment for them to execute quickly is a good place to start.

In some companies I've worked for, they've developed a good idea about what the following year's roadmap looks like before December. Take advantage of the dead time at the end of the year to refine your roadmap in a way that will keep your organization bright and shiny.

When you make the time to audit, you lessen the chances of your future products' time to launch (or debug) being bogged down in technical debt that unexpectedly becomes a showstopper. Phil Araujo - The Product Hero gives an in-depth look at the consequences of the types of debt in an organization.

As debt of any kind builds up over time, it becomes more difficult to pivot quickly when the market needs you to. And if you've been paying attention to what's going on in the world, your market, whatever that is, is moving extraordinarily fast. Welcome to the new normal.

I can't help but think about a company I used to work for about a decade ago. A little while before I left, they got a large contract to make software for a client. This new software, if I remember correctly, was scheduled to be delivered in 2025. When I first heard this date, I joked that we'll all be robots by then, and it turns out I'm not so far off.

This place was very much a feature factory led by middle managers who had no aspirations beyond being there until retirement. I wonder if this year, after it became apparent that the future of a homescreen is a voice prompt with an LLM back end, they were able to pivot to build a framework that would be able to integrate such a feature in the nearish future. Or if that mega client decided to cut their losses and contract a Stripe-like startup that can build for a future environment.

Technical and product debt of a legacy organization isn't the only boogeyman to be afraid of. Losing sales because of a drastic website change is surprisingly damaging, as Toni Koraza goes over in this article. I had no idea! But it makes perfect sense. So get your periodic updates in order.

Other audits to consider:

• Legal — Will any public-facing claims you published this year get you sued into oblivion? Are you navigating the intellectual property environment correctly? What contracts are due to end in the following year?
• Clients and users— Who have you outgrown? Can any of your relationships be nurtured more? Do you genuinely know what their needs are?
• Sustainability — Do you have a business model that will make money despite running expensive algorithms and ads? How are your company values in line with creating an ethical corporation of the future? Are you able to objectively measure value vs. costs?

Not everything needs to be done in-house. You can hire a consultant to help you streamline your priorities and review the biases in the existing algorithms and human decisions, but you must be prepared to put aside any bruised feelings. In fact, if everyone around you is only telling you what you want to hear, think about diversifying the wise voices at your disposal.

Don't repeat the mistakes of the legacy companies you hated working for in the past. Things are going to move much faster now, and you need to keep your systems clean and flexible.

If you need some advice on getting your data career started, check out my article from earlier this year.