At our user summit last summer, one of our customers ended her keynote with the words, “Are we connecting the dots, or just collecting them?” Over the past two decades as a data industry analyst, I have never heard a better summary of the enterprise data challenge of running business intelligence.
Twenty years on, I see that many of the data challenges remain the same: unless organisations start with effective data management, they will struggle to scale and they will hamper their digital transformation as a result. At the same time, organisations have more mature data infrastructures and many of the challenges involve stitching disparate data sources together and creating an overall data value chain. This is actually quite complex as organisations also need to consider security, privacy, regulatory requirements, and governance.
One of the key challenges is to address the complexities that exist for data assets across the organisation. Organisations look to data integration and data quality, but don’t always take an iterative approach to design and maintenance. The reality is that the only way to solve data challenges is to make sure that iterative processes are in place to support the organisational focus on continuous improvement.
Using data to improve customer experience
The overall repeatable success I have seen from organisations is when they use their technology as an asset and not simply as a tool. In essence, this means looking at technology and data as a way to drive success. Organisations that actually want to differentiate with their customer experience need to understand their customers. This means analysing historical information, demographics, products/services, customer satisfaction, and the list goes on. This can be complex, but if organisations look at the data they need and how stakeholders need to interact with information assets to get that view and understand the customer story, they can succeed. Organisations that understand the concrete value of data about customers, products, revenue, and how leveraging it relates to business, are more likely to plan with outcomes in mind.
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Moving from collecting data to business intelligence
The better an organisation’s visibility into their data and the easier access they have, the better able they are to make good business decisions. In my early career, seeing organisations cut costs, increase profits, or lower customer churn was the proof point I needed to shift my focus from business process re-engineering (BPR) to business intelligence (BI) and analytics, as merging both skillsets was a great way to work with organisations and support them through their data journeys.
In the not-for profit sector, business intelligence data and analytics are really important for showing donors how their funds are being used, so that they continue to invest in causes that they support. As an example, the largest privately-funded not-for-profit organisation in the world, United Way, ensures good governance by conducting regular studies to collect data on donations and outcomes.
United Way realised that the entire staff would be more likely to recognise patterns, dependencies and anomalies in data, if it were presented visually through dashboards, maps and charts. However, data scientists are a scarce and costly resource, so United Way looked for a way to share the findings from its impact studies, so that employees worldwide could learn best practice from their peers and identify opportunities for improvement.
The PerformanceLink portal makes data easier to understand: helping staff to use it to meet the urgent needs of beneficiaries. Customised scorecards and topic tabs reveal performance by population grouping and allow United Way employees to more easily track the effectiveness of charitable activities. Employees can also use the portal to create their own dashboards and reports. Data can be visualised in scatter plots, with colour-coded quintile scores revealing performance by population groupings. The portal automatically customises data based on the user’s security clearance, location, interests and needs.
The self-service data analytics portal takes the information from United Ways’ national research studies and displays performance, demographic, and socioeconomic information on digital maps, charts, graphs and reports.
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Digital maps depict where United Way is operating within areas affected by natural disasters, or ongoing deprivation, and this allows employees to map ‘need indicators’ relative to these geographies. The powerful analytics and clarity of data visualisations have helped United Way to optimise the distribution of financial and human resources to support those most in need.
The PeformanceLink portal is now accessed by more than 13,000 authorised users to see the results of the national research studies. Dashboards allow them to drill down from an entire geographic region to an individual United Way affiliate. They can also use location analytics to depict data geographically. These dynamic displays make it easy to see how each affiliate is performing relative to its peers.
The rise of embedded analytics
I see more solution providers developing partnerships and options to leverage data virtualisation, as well as more flexible information access points to ensure that organisations have access to an experience that has a similar feel to accessing a single platform. I think that this will help support organisations as they leverage embedded analytics more broadly.
I believe that verticals such as healthcare, insurance, and government will require more data sharing and open data ecosystems to deal with the after-effects of the COVID-19 pandemic. This also applies to the way we look at data privacy and security of data across geographic locations – for instance, within international travel. At the same time, the way business is conducted and the competitive landscape may shift. Analysts are speaking a lot about the need to adjust to a post-pandemic economy. Within organisations, the need for analytics to drive business decisions will increase and become more important. Organisations that were not data-driven will need to become so in order to remain competitive.
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There is an increasing focus on open ecosystems and broader access to data across platforms by creating partnerships and API access with competitive and cooperative technologies. I think as this data access increases, and organisations continue to look to cloud-native applications and integration for support, embedded analytics will become more commonplace and shift the way we consume data.