The Future of Work: How Data Analytics and Computer Science Will Shape the Business Landscape
Today, business leaders make decisions based more on available information than on “gut feelings.” That’s a good thing because informed decision-making is faster and more accurate and helps differentiate between winning businesses and also-rans.
That situation arose thanks to data analytics and computer science. As computing power increased in the last few decades, so did the volume of data available to business owners and decision-makers. In turn, that explosion drove new insights into everything from customer expectations to manufacturing materials and techniques.
Data analytics and computer science enabled improved quality, enhanced customer satisfaction, increased business agility, and scalability, and did so with unprecedented speed.
The field continues to evolve, as well. Understanding what the future holds is critical for charting a confident course forward.
The Ongoing Democratization of Data
Previously, data was the purview of data scientists and others in specialized roles. Today, that is less common. Expect this trend to continue as analytics tools become more common and easier to use. Data democratization ensures everyone can access information critical to their roles and delve into important insights to inform their efforts.
It has been clear for several years that data is the key to understanding a business’s customers, creating better products, and designing more efficient processes. However, the ability to act on those insights must go beyond data scientists and analysts. Every team within an organization needs tools and applications that improve their efficiency and effectiveness through data-driven decisions.
Thanks to apps like ChatGPT and Midjourney AI, AI is a hot topic today. However, while those two examples illustrate the incredible advances in artificial intelligence, they are just the tip of the iceberg. AI continues to evolve and becomes more adaptive as it does.
What is “adaptive AI”? According to Gartner, adaptive AI systems “can offer faster and flexible decisions by adapting more quickly to changes.” These systems “support a decision-making framework centered around making faster decisions while remaining flexible to adjust as issues arise. These systems aim to continuously learn based on new data at runtime to adapt more quickly to changes in real-world circumstances.”
The result is the ability to re-engineer the decision-making process. Adaptive AI will be embodied in a new breed of consumer-facing applications designed primarily for in-house use.
The Rise of Data-as-a-Service
It seems like everything is available ‘as a service’ these days, from software to IT infrastructure. Data is becoming available as a service, too. According to HubSpot, data-as-a-service (DaaS) is “a data management strategy that is used to store data and analytics. DaaS companies are organizations that provide customers with a service surrounding data – meaning data management, data storage, and analytics are the main selling points.”
DaaS allows organizations to outsource some of their more critical data-related needs and benefit from expertise, advanced technology, and unique capabilities not available to those organizations in-house.
Data Fabric Architecture
People previously saw data as individual points within a sea of information. However, today, we are witnessing a change in this view as we observe the emergence of what experts call data fabric.
According to Gartner, “the emerging design concept called data fabric can be a robust solution to ever-present data management challenges, such as the high-cost and low-value data integration cycles, frequent maintenance of earlier integrations, the rising demand for real-time and event-driven data sharing, and more.”
Data fabric isn’t static, however. It’s capable of listening, learning, and acting on metadata. It will be instrumental in bolstering trust in an organization’s data and using that information in decision-making processes at all levels.
Removing Silos and Increased Data Sharing
For data to drive decisions, it must be available to decision-makers. This requires a deep change to how an organization’s departments see that data. Traditionally, information was considered proprietary to a specific department – marketing or sales. That created silos where information flowed in but did not flow out.
To make data-driven decisions, organizations must eliminate silos. They need to cultivate a culture of data-sharing rather than holding onto information they feel is exclusively theirs.
Effective data sharing requires more than just the right mindset. It will also require a commitment to collaboration and communication, the right tools (including apps), and training for team members to share data effectively and with trust.
Data Doesn’t Exist in a Vacuum
Data points can provide important insights but don’t exist in a vacuum. Considering all data within the proper context is essential. For example, one might interpret a decline in customer purchases as indicating that a product is no longer popular. However, when taken in the context of rising inflation and decreased discretionary spending, the situation shifts and becomes more apparent.
Data analytics will increasingly account for context to deliver more accurate analyses. It’s all about exploring the relationships between data points rather than focusing solely on the data points themselves. After all, data points only tell part of the story. Organizations must see the bigger picture using content-enriched analysis to make informed decisions.
Achieving that is no small feat. It requires the right tools to store and capture information. However, it also requires the proper training for employees to build data pipelines, use analytics tools, and work with AI cloud services to process and model different data types.
A Shift Away from IT
Initially, data analysis was the province of the IT department. However, there is an ongoing shift from IT toward the business side as the focus of business-composed data and analytics moves away from purely technical considerations.
Composable data and analytics is a modular approach to efficiently managing an organization’s data structure. It stands in stark contrast to the previous all-in-one/monolithic approach previously used. A modular approach allows organizations to link data insights with business actions faster via tailored analytics experiences, democratized access to data, and related skills development for employees outside the IT department.
Complications from Regulations
As data becomes more and more important to organizations, it becomes more sensitive. Numerous regulations now dictate who controls what data and what information businesses can access with and without owner permission. California’s Consumer Privacy Act (CCPA), Europe’s General Data Protection Regulation (GDPR), and the strict regulations financial institutions must adhere to are examples.
However, as rules continue to change and data takes on even more value, expect to see more rules and regulations appear. That will foster regional data and analysis ecosystems to comply with local, regional, national, and international requirements.
For organizations, navigating this emerging world will require forethought and strategy. To ensure compliance, it will also require a multi-cloud and multivendor approach to creating technology stacks.
An Increased Need for Data Literacy
The US Bureau of Labor Statistics expects a shortfall of technical talent exceeding 1.2 million workers by 2026. As that shortage continues, organizations must look to upskilling to close the skills gap and improve data literacy across the board.
This is in keeping with the democratization trends. However, organizations must make a concerted effort to identify employees with the right initial skills and talents and then provide them with accurate training.
AI Risk Management
AI is everywhere today. However, that rapid spread can breed complacency when it comes to the risks AI poses. No algorithm is better than the data it was trained on, which opens a veritable Pandora’s box of potential risks. Human/machine interactions compound that.
McKinsey states, “Executives often overlook potential perils or overestimate an organization’s risk-mitigation capabilities. It’s also common for leaders to lump in AI risks with others owned by specialists in the IT and analytics organizations.” Both leaders and employees must build their risk-recognition skills regarding AI, to ensure accurate training, use, and interaction with these powerful algorithms.
Data analytics and computer science are here to stay. However, they continue to evolve, as do the ways organizations use these processes and the skills employees require to employ them.
We’re moving inexorably toward a future where widespread applications and other tools make data-driven decisions possible for everyone, not just IT departments and executives. That promises a dramatic shift in efficiency and effectiveness. However, it requires action from organizations to prepare teams for that bright future. Organizations risk behind left behind without concerted, strategic action to close skills gaps and develop new knowledge.