Data analytics and Business Intelligence (BI) solutions come with a promise of unlocking meaningful insights to accelerate breakthroughs in productivity and performance. One way to fuel growth and stay a step ahead of disruption is by embracing non-traditional approaches to data analytics.
A recent report by McKinsey & Company states that most companies are capturing only a fraction of the potential value of big data and analytics. One of the reasons for this may be because companies are struggling with identifying what data is actionable and what is insightful for their businesses.
Having the ability to connect, orchestrate, and assess data from various sources will undoubtedly unleash a great deal of potential. But for enterprises who want to lead with the power of data, prioritizing data literacy is a key step to unlocking future value.
The need for better business outcome is driving the rapid adoption of a new generation of sophisticated analytical technologies. Gaining analytic agility does not only improve process effectiveness but also the hyper-personalizes delivery of services - an indispensable part of marketing. The analytics space in today's digital age is overflowing with tools that offer serious competitive advantages. Let us take a look at some of the techniques that could uncover hidden relationships and identify undiscovered patterns in data.
Augmented Analytics: A recent report by Gartner indicates that augmented analytics marks the next wave of analytics disruption. Utilising machine learning and Natural Language Processing (NLP) techniques, augmented analytics refines raw or disparate data into valuable information. This new level of automation provides easy access to insights, allowing enterprises to proactively respond and act on data quickly.
Predictive Analytics: Having the benefit of knowing what will work and what will not, will fuel an enterprises' growth. Statistical algorithms coupled with machine learning techniques simplifies sifting through large volumes of data, finding correlations, and identifying data patterns based on historical data. Using this newly generated information helps business administrators understand the likelihood of future outcomes, create opportunities for growth while preparing for emerging challenges.
Graph Analytics: Utilising the mathematical concept of graph theory, this technique mines relationships in a distributed network of processing units, devices, assets, and end-users. Whether it is identifying the shortest path between two nodes (For example, the shortest distance between two points in the map) or creating visual summaries of data, graph analytics replicates real-world scenarios and offers insights on how elements in a network communicate and/or affect each other. This information could be used to optimize the performance of the system. For instance, enterprises could identify groups of people who have been discussing their services and what they have to say about it.
Descriptive and Prescriptive Analytics: While descriptive analytic techniques analyze the current environment or unstructured data sets, prescriptive analytic techniques use AI and machine learning technologies to suggests the most advantageous approach to tackle a specific occurrence based on that data. These techniques help outline the best possible outcome/solution as well as limitations and risks associated with it. From having timely information and understanding the impact of new procedures to reducing manual processes and providing optimal insights to the future, these techniques yield powerful data.
Behavioral Analytics: Being able to capture, profile and analyze elements of human behavior will take enterprises beyond merely predicting future outcomes to actually influencing human behavior. I believe it presents enterprises with a chance to convert their Minimum Viable Products (MVP) to Minimum Lovable Products (MLP) and turns users into fans. One of the key advantages of behavioral analytics is that it empowers data analysts to reverse engineer business objectives and work towards achieving them.
Journey Analytics: Knowing your customers, all the touchpoints and continually capturing customer feedback from multiple touch points will facilitate organizations to better understand their customer's journey and then transform it, at scale. This technique plays a critical role in bridging the gap between what a company believes they are providing and the actual experience they are rendering to their customers. Developing a unified view of the customer and delivering an enhanced customer journey will result in higher revenue growth, retention, and advocacy.
- Big Data Analytics: Driven by industry-wide improvements and open source, big data analytics is equipping enterprises with the ability to take quicker action based on data collected from several data points and digital human interaction. This analytic technique is playing a significant role in supporting enterprises move out of proof of concept phases to achieve higher levels of business value. Being integrated with other technologies like AI, machine learning, Internet of Things (IoT) and NLP, the future of big data will touch all aspects of our lives. It is making the connected world more intuitive, responsive, as well as extending convenience like never before.
These techniques not only empower companies to use real-time data to assist in the development of new solutions and realize significant cost savings, they also drive a wider range of positive outcomes including:
- Predicting and prioritizing needs/issues
- 360-degree view of individuals
- Hyper-personalization of services
- Mitigating risks and reducing downtime
- Establishing a future-forward, data-driven foundation
Advanced analytics and data science are becoming mainstream tools and solutions for companies who seek to enhance competitiveness, innovative and improve all aspects of the customer experience. The coming decade will witness more companies adopting data and analytic techniques as mission-critical systems to enhance their strategic plans. Finding answers to why a certain scenario is manifesting, what will the future hold, and how can you stay prepared for it will allow organizations to realize transformational potential. However uncertain the future is, data-backed forecasts will enable subject matter experts to proactively respond to situations and adapt to new realities in good time.