Business Intelligence Trends, It's Still About People
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Business Intelligence Trends, It's Still About People

Christopher Hutchins, VP, Chief Data & Analytics Officer, Northwell Health

Over the last several years there has been explosive growth in the generation of virtually every kind of data. The evolution of tools and technologies seems to be accelerating as disruptive innovation continues as it has since the dawn of time. We now have amazing compute power and data capacity in the palm of our hands that we depend on in ways that we couldn’t have imagined a little over a decade ago. This is only one example of multiple technologies and services that are now commonplace and fueling this growth. This has created the demand for skills and technologies to help us figure out what exactly to do with this data and what it all means. As I look across industries I see some common approaches to managing this that are having tremendous impact and success.

Data Visualization

There has been a significant proliferation of powerful data visualization tools that are industry and data source agnostic. This is making it easier to produce the kind of outputs that will turn heads and can add potential value. These typically are presented in dashboards and scorecards and contain content from more than one data source in a single view. In most industries these data domains are managed separately and analytic outputs are accessed in multiple tools and platforms. Producing a consolidated view of key measures across an organization has historically been a manual effort. This generally required assembling outputs from disparate systems and domains in order to develop and produce these views. This can often be complex due to dependencies that domains may have on other domains completing in sequence.

These technologies are now enabling high availability of consolidated views with the most current information available from multiple domains at whatever refresh rate the organization determines for each domain. Dashboards within a single domain are certainly valuable to leaders managing their operations. Consolidated views enable insight into multiple business units and domains at a single glance to more easily assess enterprise performance.

Enabling the generation of these insights is not only possible but it can provide a competitive advantage. Organizations generally have multiple significant disciplines that are generally not interchangeable and all are required to provide the full range of services that are required for success. Within each of these areas there are also leaders and staff who monitor, understand, analyze and respond to data that is generated to support the functions within their purview. Bringing these experts together is key in order to establish vision and collective ownership and responsibility for the quality within each domain and ultimately the consolidated views. This can certainly be a change management challenge. The additional challenge is in choosing and establishing an enterprise platform to land and organize the data to support both. While these outputs can look flashy and official, they may not provide meaningful or actionable insights. It will be increasingly important to ensure that teams remain focused on data quality and standardization both within and across data domains.

Self-Service Analytics

Analytic capabilities have been advancing at a rapid pace and are empowering and accelerating the ability of organizations to gain essential transformative insights. These insights are enabling them to sustain, innovate and grow business in a very competitive and pressurized environment. There are many factors that are driving the need for this capability. Specialized verticals exist in most organizations that require career level focus from a subject matter perspective that is not easily centralized. Prioritization of analytic development across an enterprise is also challenging as there are often competing initiatives. Prior to the recent advancement of self-service tools, it simply took too long to perform the simplest analyses and business leaders were often stuck in a development queue. These factors aside, self-service and independent action with data decentralized across an enterprise can rapidly lead to data chaos and anarchy resulting in analytics that cannot be trusted.

Reigning in or preventing this chaos requires thoughtful strategies that do not impede business units within an organization from effectively managing their areas of purview, nor should they prevent the advancement of their analytic capabilities. There may be a temptation to stick with more traditional approaches that keep tight control on data access and business intelligence tools given the explosive growth in the generation of data of virtually every kind. It is becoming increasingly difficult to ensure data quality and accuracy while at the same time providing the level of access to data that a business demands. If the objective truly is to enable the business to be successful then a solid strategy is needed. This strategy must enable business units to evolve and adapt to a landscape that is rapidly changing and seeing exponential growth in data generation.

Centers of Excellence

As an organization goes through a transformation data driven decision-making, there is often sensitivity to centralizing development and support of analytics. Establishing and marketing this as a service to support analytic teams is highly recommended. Centralized infrastructure can also assist in developing governance around organizational analytics without impeding the important work being done in business units. In addition to providing this capability, it is also important to provide framework that enables teams to collaborate, share, and promote best practices. One approach is to establish a center of excellence or an analytic resource center where teams can contribute content to be shared with colleagues. I have also heard this function being referred to as a data concierge service. Including a platform to house a business glossary where common terms and definitions are published is also recommended. Additionally, publishing links to important documentation or to regulatory information will also add value. Identifying a business leader with broad organizational knowledge to lead this effort can accelerate adoption and improve governance with a focus on developing shared content and the development of user forums and communities. Over time this function can grow to become the intake valve for analytics across an enterprise as networking progresses to connect domain experts throughout.

Artificial Intelligence & Machine Learning

New technologies and solutions generally identified as Artificial Intelligence (AI) are appearing nearly every day it seems. The topic elicits a wide range of responses ranging from fear and skepticism to hopeful enthusiasm and certainly many others in between. As with any new technology there is good reason for all of these initial reactions and perspectives which I believe are very healthy when they exist together in leaders and teams that are charged with guiding strategies to develop and implement solutions. The need for this capability is only increasing as the pace of data generation continues to accelerate with accumulated data nearly doubling annually.

While the possibilities are tremendous, it can be challenging to identify an appropriate entry point that satisfies the desire to move forward and provides an acceptable risk level at the same time. Many industries are starting with robotic process automation which is in early stages being used to automate rule based transactional functions. This includes banking, retail, marketing, health care and customer service to name a few. There are numerous potential applications for sure.

“As an organization goes through a transformation data driven decision[1]making, there is often sensitivity to centralizing development and support of analytics”

At the forefront of the challenges with these technologies is privacy and security. While some industries capture consent at the time of an individuals’ first engagement, concerns remain with regard to how data is used, who owns the data, the insights, and work product of the models and solutions that are developed. The potential utility of the data is generally not understood by an individual at the time consent is given. Consumers are becoming more educated, especially when a data breach makes the headlines.

Effective Data Management Strategy

Each of the topics I have discussed here have complexities and challenges of their own but all share a dependency on effective data management strategy. One thing that is very clear is the criticality of ensuring focus on data quality, privacy and security, and effective data management strategy. Deploying a strategy that ensures enterprise data assets are well curated, standardized and fit for use is perhaps one of the most challenging to achieve but it is essential.

Its’ Still About People

Regardless of industry, there are multi[1]faceted organizational dynamics and consumer interactions that must be understood in order to make progress in any of these areas. While some may have apprehension and fear of technology replacing jobs, I submit that people and relationships are becoming more essential and not less. These tools and techniques can accelerate the derivation of insights for sure but they will aid rather than replace human decision making and likely enable more decisions and faster innovation. I believe that the pace of change will continue to pick up speed. It is not the technology that leads me to conclude this. Rather, it is the amazing ability of human beings to adapt, innovate and create that will produce the next waves of disruptive innovation.

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