For Tech Upgrades, Big Leaps Forward are Exciting – But Slow and Steady Wins the Race


In some ways, managing a business is like piloting an aircraft. Some systems need maintenance, some parts need occasional upgrades or replacements, and everything gets easier with experience. As long as changes are made one at a time – a new engine or navigation panel, for instance – the pilot and crew can adjust.

But if everything changes at the same time, all while the plane is already in the air, too much confusion will result. Even if every individual change involves an old part being replaced with an objectively better one, no one will have experience with the new system, and the entire flight will be in a lot of trouble.

Businesses face the same danger. For all the (well-deserved) talk about big data, data insights, AI, and machine learning for digital transformation, companies that aim too high, too soon, will tend to find their efforts stalling at critical moments. There is a much better and safer way to get where you need to go.

Patience, Perspective, and Priorities

By splitting business needs into short- and long-term objectives, it becomes easier to focus on the highest priority tasks. For example, if Company X struggles to identify key talent, it may simply need to make its own internal data more visible to company leadership. In such a case, their immediate priority would be to centralize key, relevant data using a public cloud like Amazon Web Services (AWS) and to adopt a data visualization tool such as Amazon QuickSight. Once productivity numbers across an entire department or organization can be compared along a standard scale, the outstanding employees can be quickly identified.

Company Y might have a pressing issue regarding turnover.  While data analysis using historical trends is typically applied by human resources (HR) teams, this type of analysis often leads to the wrong conclusion and wastes company resources on ineffective programs. Alternatively, by combining data from payroll and performance management systems, and using machine learning, the relationships (or correlations in data speak) between the data attributes can be discovered. These relationships are generally far too complex for people, even using MS Excel, to uncover on their own. The result is a more accurate picture of the reasons employees depart an organization, and deeper insights that allow HR teams to target employees directly with more effective programs to reduce turnover at a lower cost, and even predict which key employees are most likely to depart.

Learning to Fly

The important insight is that Company X and Company Y might be the same organization at different points in time. Though Company “XY” surely has many needs, trying to address them all simultaneously is costly, disruptive, and ripe for failure. Instead of changing everything at the same time, all while the plane is already in the air, business challenges that can benefit from data solutions should be prioritized and addressed in manageable stages.

Every organization faces its own unique challenges. At AMPOS, we spend time with our clients before every project to truly understand their current circumstances and the challenges they face. From there, our team helps them overcome their most urgent problems, one at a time, until they arrive at exactly where they want to be.

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