As more companies invest in data and apply analytical insights, they start to demand better products and services from their vendors. Why should a business use one data service when another startup has launched a competing product with better features?
Additionally, as business leaders learn more about what their data can do, they challenge teams to build better systems to accomplish their goals. The result is that the “data-sphere” continues to evolve, with each year bringing new changes. Learn about five major changes in 2019 that are improving how we use data.
Analytical Insights Are Moving
From Reactive to Proactive
A common joke in finance suggests that “your forecast is wrong as soon as you complete it,” which speaks to the fickle nature of predictive insights. Many finance and data professionals will spend hours trying to map out the next quarter or the next year, only to have their forecast ruined by an unexpected event such as a natural disaster, bankruptcy, or acquisition. However, as analytics and data get smarter, our insights are getting better.
Predictive analytics can be used across all levels of an organization. This form of analytics can help a small marketing team anticipate demand or work to identify patterns in customer behavior. Brands that invest in these predictive insights can take steps to prepare for the future and may be more resilient in the face of controversy. These predictions are more valuable and forward-facing than traditional analytics, which were historically more reactive and reported on past events.
More Companies Are Investing in Machine Learning
Consider the statistic that 73% of all data is wasted within an organization. Today’s companies want data, but they don’t know what to do with it. Part of this conundrum has to do with resources. With so much data available, no single person can sift through it all, even with endless tools and systems with which to navigate through it.
Machine learning has stepped up in a big way to improve data analytics within organizations. A person can’t sort through volumes of data, but a robot can. Machine learning tools have become significantly more accessible over the past few years to the point where small businesses can use machine learning to accomplish their goals. The insights these bots glean make the data more valuable because less time and resources are wasted.
Brands Are Creating Connected Views of Their Data
As data systems mature, brands are improving their connectivity to obtain better insights. Instead of checking analytics from multiple sources and trying to connect the dots, business intelligence is working to connect the data sources into one simplified source.
At its minimum, this unified source allows everyone to operate on the same page with the same information. This cohesiveness improves collaboration and keeps employees focused on a few single priorities. However, unified insights that combine data give companies the information they need to create better decisions. Better decisions lead to improved performance and increased revenue.
Companies Are Moving Beyond Their Initial Legacy Systems
We are starting to see the next generation of technology investment. At first, companies were investing in tech because they wanted to stay relevant. Now, many of these first systems are outdated and unusable. This reality is leading brands to improve their data and insights through better tech by replacing clunky legacy systems with new tools.
For example, a financial services provider can better work with clients through a system such as Forex trading. Modern analytical insights paint a clearer picture of the risks and return on investment. Forex is a replacement from initial technology investment into digital trading and an upgrade now that firms know exactly what they want with their financial services.
The Demand for Business Analysts Continues to Grow
In the early days of data, analytics management was the responsibility of anyone who needed an answer from data. Many accounting or marketing professionals worked to understand data management on their own and lobbied senior leadership to allocate resources to analytics improvement. However, as the value in data insight grows, more companies are creating separate roles — and separate departments — for analytics.
For example, the U.S. Bureau of Labor Statistics has estimated that the job growth for management analysts will grow faster than average over the next decade. A 14% growth rate will lead to approximately 118,300 new jobs and a median salary of $83,000. Data analysts are standing on their own within organizations and proving their value with each new insight and suggested action.
Many companies have so much data that they don’t know what to do with what they have. As more brands invest in data management and analytics, this field will continue to grow, and more tools will enter the market to help brands thrive with their data.
