Improving the customer experience and creating impact with products centered around human values require organizations to move beyond traditional data and analytics strategies to lay the foundation for insights-driven change. To build an organization that maximizes the values of multidisciplinary teams, diverse data sources and omni-channel support, it’s important to strip the insight-generation process to its bare bones and understand the very basic concepts: data, analytics and insights.
What is Data?
Data is individual facts or pieces of information, generally expressed in their most simplest form. For example, a health insurance company collects the following data about its members: first name, last name, and phone number. Individually, each data point is not very useful, but collectively they provide a lot of information about an entity; in this case, a patient.
In the technology industry, we categorize this type of data as structured data, because it can easily be tagged, organized and stored in a structured format such as an Excel workbook or a SQL database. Collecting data is the first step in building a data-driven organization.
What is Big Data?
Big data refers to very large sets of data that are generated in exponential amounts, and come from a variety of data sources and formats – some unstructured.
A health insurance company usually collects vast amounts of data including claims information, emails, images, web analytics and patient history. To extract valuable insights that solve complex business problems and have a real impact on people, it is important to tap into diverse sources of data.
What is Data Analytics?
Data analytics and traditional business intelligence derive correlations, analyze trends, and answer specific business questions. Data analysts produce bar graphs, lines graphs, pie charts and scatter plots to allow business leaders to visualize data and extrapolate information.
A health insurance company will use data analytics to analyze claims in different market segments, identify trends in patient conditions and yearly medical spending. It would employ advanced analytics techniques to calculate costs for different coverage plans based on these trends.
What Are Data Insights?
Data insights are the knowledge and information that result from data analysis and experience. Deriving insights requires judgement and discernment over quantitative and often qualitative data analysis. The organization substantiates insights with an understanding of the customer through data, as well as empathy. Data insights are often referred to as actionable insights, since they form the basis for strategic plans and roadmaps.
A health insurance company will leverage its data and insights to design services that improve patient health.
Where Does Data Science and Machine Learning Fit in?
Traditional business intelligence and data analytics are founded on statistical methods and rely on programmers define all the input data points and rules to produce a specific result. Machine learning models extend this capabilities by providing the infrastructure to process large amounts of unstructured data and the continuously “learn” and adapt with new data and changing circumstances. Data science allows analysts to uncover associations and correlations that would be missed in traditional analysis, an indispensable asset when building an insights-driven organization.
A health insurance company is likely to implement data science models to predict customer churn. The insurance provider can feed demographic, claims, call center and complaints data to a model to produce a highly accurate list of customers that are at risk of canceling their service.
How Do You Build an Insights-Driven Organization?
In order to create an insights-driven organization you must first ensure it is a data-driven organization. According to a Forrester report, Now Tech: Insights Providers, although business leaders recognize the value and potential of data, most still rely heavily on past experience and intuition to drive decisions. The following are the key steps in building a data-driven organization:
- Collect data. Think beyond the data you currently collect. Are you doing enough surveys? Do you do A/B testing when releasing products? Are you engaging with your customers in social media? Collect data that matters and that can ultimately drive better insights and business outcomes.
- Make data accessible. Important data should be made accessible throughout the organization via flexible platforms and protocols such as APIs. Otherwise, collecting data is a useless endeavor and a waste of resources.
- Use data. If the data is accessible, use it, Clean it, scrub it and use it to make informed decisions, to identify bottlenecks, prioritize activities or to trigger new thinking. If people don’t use data, then it may stop being collected, and if you decide later that you need it, it may be too late.
Building an insights-driven organization requires going beyond an academic analysis of data and focusing on what really matters. You can easily get absorbed and overwhelmed with never-ending data analysis and large amounts of data visualizations, unless the analysis is focused on getting right to the heart of a problem.
- Understand the customer. Building empathy with the customer base will be key to a qualitative analysis that can complement the quantitative to generate insights. Making continuous efforts to understand customers is the first step to solving a problem that matters to them.
- Embed throughout the organization. Insights should be used in every department across the company, from the top down: to define company strategy, design new products, create marketing campaigns, identify process improvements and measure the impact of every activity.
- Augment human capabilities with AI. Companies can take the power of their data to the next level with artificial intelligence. They can their workforce’s capabilities by automating business processes or drive value to the customer experience with smart personalized algorithms.
- Embrace iteration. Insights change as people grow and evolve. Companies should embrace an iterative process where they engage in continuous learning and improvement from insights. This allows them to evolve alongside the people they serve.
- Lead organizational change. Employees should be encouraged to collect data, search for insights and change the way the business works. It requires leadership and change management to create a positive company culture around data-driven insights and decisions.
How Can an Insights Service Provider Help?
Building an insights-driven organization may sound like the latest buzz word to businesses that face the following common barriers:
- Silos in the organization. Business that have multiple departments often work in silos. Data collection efforts are duplicated across departments and rarely shared. Bureaucracy creates communication barriers that make people gravitate toward silos.
- Data is not accessible. Data that is collected is rarely readily accessible to people outside the department through APIs or shared platforms. The need to request data delays access to the latest and most reliable information. Making data accessible requires a robust data governance and infrastructure strategy.
- Technologies evolve at a rapid pace. For most businesses without sophisticated IT departments, keeping up with all the new technologies is nearly impossible. Specialized talent is scarce, abut it is critical to creating digital solutions as the business grows and customer needs and expectations evolve.
- There is simply not enough time for all of this. Meeting the needs of the core business leaves no time to learn new technologies, implement new business practices or take on new strategic projects. Executing a strategic vision often requires help from technology partners.
Insights service providers and digital transformation consulting firms like Wovenware can help to provide specialized talent to help your business evolve into a data and insights-driven organization. From building a data strategy around business opportunities, to creating sophisticated artificial intelligence models, bringing in an expert will help drive organizational change, set a robust data infrastructure, and accelerate the time to extract value from data insights.