With 2022 well underway, many businesses are deploying new data-driven strategies and models to promote growth, accelerate digital transformation, and increase operational efficiency. However, to maximize success, enterprise leaders must incorporate decision intelligence and data-driven insights into their decision-making processes.
Decision intelligence is an approach that can help ease the burden of data analysis and empower every individual to make insights-driven decisions. Decision intelligence augments human decision-making with AI/ML to generate faster, better business insights from enterprise data.
The right data insights enable teams to unlock new sources of value and deliver better services to customers. They also allow managers to dig deeper into problems and find solutions that mitigate risk. The problem, of course, is that extracting useful findings from data is easier said than done.
Many organizations today struggle to get timely insights from their data. Legacy analytics tools are often better at providing reactive, rather than proactive, insights. On top of that, many companies don’t have enough context around their data to make confident decisions or hypotheses. Data tends to give us the “what,” not the “why,” behind business performance.
As a result, it’s up to data teams to uncover the full story, make connections between causes and effects, and recommend changes that improve long-term outcomes. But with data talent in short supply, organizations need more than numbers; they need robust approaches that can organize, interpret, structure, and present data in meaningful ways. They also need tools that all individuals throughout the business can use, regardless of technical expertise. The more people who can access data, ask questions of data, and iterate on insights, the better decision-making becomes in the organization.
Here are five ways organizations can use decision intelligence to gain competitive advantage and become more data-driven in 2022.
Automated Insight Generation
Thanks to the rise of cloud computing and IoT technology, companies have more data at their disposal than ever before. In fact, many businesses don’t have sufficient resources to manage it well. When combined with the reality of our growing technical skills gap, automation is one of the best answers we have for analyzing data at scale.
Automation solves many challenges when it comes to data analytics. Teams can quickly analyze all possible combinations of data across all available variables, and analysts don’t have to manually conduct one-off SQL queries to test individual hypotheses. They can also avoid bringing their own biases to their data analysis and instead trust in decision intelligence to study all factors objectively.
For example, consider an e-commerce company that sells goods in multiple regions. Decision intelligence could quickly and automatically segment sales data by location and age group and combine that data with promotion schedules, discounts, or other potential variables to determine where sales efforts were most successful. Using decision intelligence, business teams wouldn’t have to dig into each region individually or risk missing a valuable insight buried in complexity.
Moreover, automated insights can also provide answers to questions that teams don’t even know to ask. These modern analytics tools can spot new trends at a granular level and identify reasons for those trends emerging, then proactively provide those insights to teams.
Natural Language Processing
Natural language processing technology has improved tremendously in recent years. When applied to decision intelligence, allowing users to conduct queries or analyses in human language in text or voice. For instance, people can ask their decision intelligence software questions like, “why are sales down in New York?” and receive clear responses back along with compelling visualizations.
Consequently, business users can get the answers they need to fully understand what’s happening without having to perform complex analyses by hand. People in all departments can figure out why certain metrics have changed or why performance is down over a certain period. Natural language processing is democratizing data analytics by empowering more people to have access to advanced analytics capabilities, regardless of their skill level.
Machine Learning at Scale
Building on the idea of democratization, decision intelligence software supports upskilling initiatives so that all employees can help navigate the larger data ecosystem. With the ability to deploy machine learning at scale, enterprises can overcome the technical skills shortage by equipping more stakeholders with easy-to-use analytics tools.
When machine learning is accessible to more people, organizations can close the insights gap that plagues so many businesses today. Data scientists and data engineers no longer bear the sole responsibility of providing data insights throughout the organization, and business users can participate in the iterative process of discovering new insights.
Decision intelligence provides a level of explainability and transparency, so users can interpret results, gain confidence that insights are accurate, and understand how data is tied to business results.
Real-Time Analytics Within Cloud Data Warehouses and Lakes
In a modern data stack architecture, it’s critical for tools in the analytics layer to leverage the compute power of cloud data warehouses in the storage layer. Most organizations prefer to not move their data to the analytics layer and would instead prefer all the processing to take place in the data warehouse or the data lake.
Decision intelligence solutions can integrate directly with data warehouses and data lakes, creating a more streamlined and efficient approach to data analytics. Business users can perform live queries on data warehouses and advanced analyses directly within their data lakes via machine learning programs. By ingesting massive volumes of data from diverse sources directly into their cloud-based repositories, there is great potential for richer insights.
Virtually Unlimited Processing Potential
Unlike traditional reporting and dashboarding systems, decision intelligence is architectured without data size limits. While older analytics platforms typically work best on limited data sets and aggregated data, decision intelligence thrives on analyzing non-aggregated data.
AI- and machine learning-driven analytics tools are uniquely equipped to study seemingly unrelated variables. These technologies can uncover detailed insights and patterns that would otherwise go unnoticed, allowing organizations to gain a more comprehensive understanding of the broader business landscape. And, as mentioned previously, all of this can happen automatically.
Decision Intelligence Is Paramount in a Modern Data Strategy
Modern data strategies are incomplete without robust analytics capabilities that produce timely, proactive, and granular insights. Leaders today need more than cold hard facts and figures. They need context, real-time intelligence, and automated tools that draw out the “why” and “how” behind what’s happening. And they need these capabilities packaged up in a way that allows all business stakeholders to take advantage.
Only through AI, machine learning, and natural language processing – bundled up in powerful decision intelligence software – is this possible. Decision intelligence is the answer for leaders who want to unleash the full potential of their data, make better decisions, and create new sources of value for their enterprises.