Data and analytics are woven into the fabric of doing business for most organizations, but even in the more mature technology deployments, we still see emerging trends – such as artificial intelligence and machine learning – to help leverage data more broadly for even greater value. Just as important, we’ve also gained clarity around technology adoption and use in 2019 and beyond.
At the start of a new year, it’s good to take stock of where you are and where you want to go. Many of the organizations we talk to are asking: “How can we finally harness the power of our data and build a strategy to take our data and analytics investments to the next level?”
Assess your own data and analytics strategy and your ability to gain quantifiable business benefits by looking at it through the lens of these 5 hot trends:
Data and Analytics are Finally Recognized as the Original Power Couple
Data and analytics initiatives tend to be managed separately, meaning that project sponsors will look at how to manage their data more effectively based on perceived challenges, or how to deliver analytics more effectively, but rarely take both into account for the same project. The adage garbage in, garbage out, is generally an afterthought. Forward looking organizations, on the other hand, understand that to achieve analytics success, strong data management is required.
Unfortunately this has led to organizations having to piecemeal their initiatives to take advantage of both data and analytics. Understanding that both are required for success also leads to businesses developing cohesive strategies to align their data and analytics initiatives to ensure that quantitative business value can be realized. As organizations move from struggling with data challenges towards delivering strong analytical outputs, data and analytics strategies will become more closely aligned more broadly across the organization.
From the vendor side, support for a full-scale approach to analytics will continue to improve. Essentially, solution providers can no longer be a one trick pony. To provide real value to customers, the ability to provide strong data management intuitively will be both expected and required. This, in turn, will lead to a convergence of data and analytics strategies.
Next-Gen Embedded Analytics Deliver Next-Level Customer Experience
Embedded analytics is becoming the key way to delivery operational analytics to a broad set of users across the organization. In Dresner Advisory’s Embedded Business Intelligence Market Study, over 90% of respondents feel that embedded analytics is important to their analytics environment. Ventana Research estimates that by 2021, over half of analytics deployments will be through embedded analytics use cases. As more organizations leverage internal embedded analytics applications, new uses for embedded will be developed.
Next-gen embedded analytics apps will take the promise of embedded analytics to the next level. Operational intelligence offers a current view of the organization, but providing added value to customers and embedding analytics into processes to gain better visibility into customers and to provide better customer experience, provides quantitative value to embedded analytics use. Whether an organization decides to develop customer facing applications or leverage embedded analytics within internal applications to gain better perspective on customers, holistic customer insight and experience are what will ensure embedded analytics converges with customer-differentiating processes to deliver full visibility of customers internally and consistent, better customer experience to enhance branding and overall customer value.
Smart Ecosystems Redefine Your Data Strategy for the Better
Big data, IoT, and more diverse data sources enable more complex data ecosystems. With the rise of smart cities, autonomous vehicles, sensor data for supply chain management, chat bots, and the list goes on, the market will see a shift in how technology interrelates to create an increasing number of autonomous ecosystems. Integration of diverse data types coupled with more digitization and automation will shift the way we interact with technology. Affecting our personal lives first as organizations decide which technologies to adopt and how to integrate disparate data sets more broadly.
Within industries such as manufacturing, supply chain, and healthcare, applications are more obvious as sensor data being collected is already leveraged for analytics. Increasingly interactive and intuitive technologies that we use in our personal lives will start to converge more broadly within businesses so that organizations can take natural language interactions and information access to the next level.
Artificial Intelligence Gets a Helping Hand from Human Intelligence
I keep reading articles and blogs about the rise of AI and how robots will take over our jobs and change the way we work and interact with technology. In essence, the fear of losing jobs to machines is real. The reality is that there will be shifts in the way we interact with technology and the level of automation within society. Yes, there are certain industries and jobs that will be replaced by automation. At the same time, although artificial intelligence and machine learning will allow computers to learn from data and outcomes, in many cases humans will still need to be leveraged to gain better insights into data. Basically, there may come a time when computers are smarter than humans in relation to problem solving and time to insight. But – AI cannot take the place of morality. Even if programmed, once machine learning takes over, it becomes impossible to make sure that AI will make the right or best human choices.
This is where the collaboration between human intelligence and artificial intelligence takes over. Technology will not replace us but will support our ability to make more informed decisions. How we choose to leverage AI is up to us. Many organizations are looking at using AI and ML to develop predictive models and create autonomous ecosystems. Automation, supported by AI, enhances process efficiencies. At the same time, without having to spend time managing data, humans can spend their time being more dynamic in the choices they make and taking advantage of what technology has to offer.
This is the promise of human and artificial intelligence in harmony. As organizations start applying more ML and AI and experiment with models, they will be able to leverage their human capital more efficiently.
The Industry Catches Up to the Reality of Insights at Scale
The reality is that, as an analyst and consultant, when organizations needed a solution that could scale across the enterprise and be leveraged by several types of users and large numbers of users, Information Builders always made my shortlist. The reason for this is that performance and scalability have always been essential for long-term analytics success and is built in to their platform. With the ability to leverage more data than ever before, tie analytics to operational processes, and deliver solutions to more people, the market is finally catching up. Not only are solutions starting to focus on providing these capabilities, but organizations understand the value of a more holistic approach to data access across the organization. Consequently, not only will the market continue to work towards delivering broader platforms that are enterprise-scale, but organizations will get to the point they no longer want to take a piecemeal approach to their analytics initiatives.
The bottom line is that to gain actionable insights, organizations require broader data insight and more people that have access to the data they require in a flexible way. The market is finally providing what should have always been there and analytics maturity is also demanding broader use.
As computers get smarter, so do we. These trends highlight the fact that, by taking advantage of advancements in technology, we can create more flexible data and analytics environments. This added flexibility links data and business value and gives us better visibility into our customers, partners, supply chains, and services. It helps create tighter alignment between analytics and business processes, and if done properly, can give us better insights and competitive edge.