The power of data has increased exponentially over the last decade, and by all indications, shows no signs of slowing down. As organizations find new ways to leverage data and analytics, they become more empowered to apply these insights to their decision-making, ultimately giving them more control over outcomes and helping them steer their businesses in the right direction.
With that, having a strong handle on data and analytics capabilities has never been more crucial for business than it is today. Below, some of AHEAD’s best minds in the data analytics space share their thoughts on what’s to come in 2022 and what organizations can do to remain ahead of the curve.
Matt Sweetnam, Principal Technical Consultant
Prediction: Operationalizing Data Science
2022 will be a year where many organizations try to figure out how to operationalize data science. We’ve all done the cowboy analytics, and now it’s time to make the outcomes work within the organization without losing the spark of innovation and ingenuity that has been the hallmark of successful data science to date. This will entail reviewing existing environments, identifying gaps in process, tools, and people, and making necessary adjustments.
Eric Mader, Principal Technical Consultant
Prediction: Human-centered Data & DCoE
2022 will continue the trend of organizations becoming more focused on human centricity, via the continued adoption of frameworks and methodologies such as design thinking and empathy mapping. These practices have been popular with software development teams for years, but leaders are recognizing that addressing the needs of actual human end users and delivering real business value will require participation from all areas of an organization. Data analytics will be no exception. It will no longer be acceptable for analysts and business intelligence experts not to have an understanding of the actual business questions that must be answered before building any technical solution. Nor will it be acceptable to use legacy delivery methodologies, such as waterfall, that don’t provide for iterative delivery and the establishment of feedback loops to ensure that questions are actually being answered.
Data analytics expertise will also become more embedded into the day-to-day work performed by technical teams. This will become a growing necessity as organizations continue adopting the cloud, which requires a shared responsibility model with providers. This model keeps data sources tied to the specific cloud solutions, disconnected from centralized data repositories and silos. In order to tap into this data within these platforms, a breadth of data knowledge will be required. In the coming year, more organizations will begin establishing Data Centers of Excellence (DCoE) in order to foster and grow best practices, empower all teams to become data teams, and accelerate their digital transformation journeys.
Johnny Hatch, Director, Client Solutions Architect
Prediction: Using Data to Drive Actionable Insights
The amount of data being generated in the world today is growing rapidly – and organizations are clamoring to capture and store that information. However, unless there is a plan to do something meaningful with those data sets, the effort and expense to amass these mountains of data will be a missed opportunity for the business. One of the big buzz words you’ll hear in the data analytics space now and in the future is “actionable insights.” Breaking down this term has 2 basic components:
- Insights – before you can do anything meaningful with your data, you have to know what you want to measure and what would be relevant to your organization.
- Actionable – once you have identified the data that’s relevant, there needs to be a plan for what to do about it. Even better would be if that plan was automated and the actions didn’t require manual intervention.
The following includes three initiatives you can pursue in 2022 to start making actionable insights a reality within your organization:
Add quality gates to your software release management process.
Most organizations use monitoring and observability tools to understand the health and performance of their applications and infrastructure running in production. However, a much smaller subset of organizations are leveraging those same tools for their lower tier, pre-production environments. Understanding how things perform before they are released into production can go a long way toward not just happier customers, but stickier, more profitable customers. Start by defining the key metrics that are important to you and your customers by creating Service Level Indicators (SLIs). Next, define a threshold in the form of a Service Level Objective (SLO) that those SLIs need to meet in order to consider a software release good enough to promote into production. If your SLIs meet the defined SLOs, automate the promotion of pre-production releases into production. If they fail the SLO test, provide a fast feedback loop to the appropriate teams outlining which SLIs did not meet their SLO goals.
Develop a data science practice.
Many organizations have dabbled (or taken the plunge) into using data for analytical purposes, which can provide an array of insights about what could happen in the future. However, with the maturing of data science practices, companies should seek to not just predict what the future holds, but prescribe the outcomes they hope to achieve. This can be accomplished by training neural networks and developing machine learning algorithms to simulate what would happen based on different courses of action. The algorithm that helps generate the best results wins—don’t just set it and forget it though. Market conditions are ever-evolving, and with new experiences come new data sets that can be used to further train your algorithms and make adjustments to your strategy and company direction.
Communicate the corporate goals and objectives to everyone.
It may sound intuitive, but a breakdown in communication is one of the biggest drivers of inefficiency for businesses today. When the mission has been clearly (and repeatedly) communicated, the power and effectiveness of a team multiplies. Has everyone on the team been equipped with the same data, enabling them to aid in accomplishing the company’s goals? Does everyone know what those goals are? Technology is a powerful tool that can allow us to better apply the use of data, but without proper communication, technology can only take us so far.
For more information on AHEAD’s Data Analytics practice, get in touch with us today.