Business, data and analytics strategies – connecting the dots or just collecting?

This is a guest blogpost by Michael Corcoran, senior vice president at Information Builders (

Speaking at our Information Builders‘ Summit, IDC Group vice president, Dan Vesset estimated that knowledge workers spend less than 20% of their time on data analysis. The rest of their time is taken up with finding, preparing and managing data, “An organisation plagued by the lack of relevant data, technology and processes, employing 1000 knowledge workers, wastes over $5.7 million annually searching for, but not finding information,” warned Vesset.

Vesset’s comments underline the fact that data must be business-ready before it can generate value through advanced analytics, predictive analytics, IoT, or artificial intelligence (AI).

As we’ve seen from numerous enterprise case studies, co-ordination of data and analytics strategies and resources is the key to generating return on analytics investments.

Building the case for aligning data and analytics strategies

As data sources become more abundant, it’s important for organisations to develop a clear data strategy, which lays out how data will be acquired, stored, cleansed, managed, secured, used and analysed, and the business impact of each stage in the data lifecycle.

Equally, organisations need a clear analytics strategy which clarifies the desired business outcomes.

Analytics strategy often follows four clear stages: starting with descriptive analytics; moving to diagnostic analytics; advancing to predictive analytics and ultimately to prescriptive analytics.

These two strategies must be aligned because the type of analytics required by the organisation will have a direct impact on data management aspects such as storage and latency requirements. For example, operational analytics and decision support will place a different load on the infrastructure to customer portal analytics, which must be able to scale to meet sudden spikes in demand.

If operational analytics and IoT are central to your analytics strategy, then integration of new data formats and real-time streaming and integration will need to be covered in your data strategy.

Similarly, if your organisation’s analytics strategy is to deliver insights directly to customers, then data quality will be a critical factor in your data strategy.

When the analytics workload is considered, the impact on the data strategy becomes clear. While a data lake project will serve your data scientists and back office analysts, your customers and supply chain managers may be left in the dark.

Putting business outcomes first

Over the past four decades, we have seen the majority of enterprise efforts devoted to back-office analytics and data science in order to deliver data-based insights to management teams.

However, the most effective analytics strategy is to deliver insights to the people who can use them to generate the biggest business benefits.

We typically observe faster time to value where the analytics strategy focuses on delivering insights directly to operational workers to support their decision-making; or to add value to the services provided to partners and customers.

How to align data and analytics strategies One proven approach is to look at business use cases for each stage in the analytics strategy. This might include descriptive management scorecards and dashboards; diagnostic back-office analytics and data science; operational analytics and decision support; M2M and IoT; AI; or portal analytics created to enhance the customer experience.

Identify all the goals and policies that must be included in your strategies. Create a framework to avoid gaps in data management so that the right data will be captured, harmonised and stored to allow it to be used effectively within the analytics strategy.

Look at how your organisation enables access to and integration of diverse data sources. Consider how it uses software, batch or real-time processing and data streams from all internal systems.

By looking at goals and policies, the organisation can accommodate any changes to support a strong combined data and analytics strategy.

Focus on data quality

Once you have defined your data and analytics strategies, it’s critical to address data quality. Mastering data ensures that your people can trust the analytic insights derived from it. Taking this first step will greatly simplify your organisation’s subsequent analytics initiatives.

As data is the fuel of the analytics engine, performance will depend on data refinement.

The reality for many data professionals is that they struggle to gain organisation-wide support for a data strategy. Business managers are more inclined  to invest in tangibles, such as dashboards Identifying the financial benefits of investing in a data quality programme, or a master data management initiative is a challenge, unless something has previously gone wrong which has convinced the management team that valuable analytics outputs are directly tied to quality data inputs.

To gain their support for a data strategy consider involving line of business managers by asking them what the overall goals and outputs are for their analytics initiatives. An understanding the desired outputs of data will then guide the design of the data infrastructure.

Pulling together

Often we see data management, analytics and business intelligence being handled by different teams, using different approaches, within the same organisation. This can create a disconnection between what the business wants to achieve from data assets and what is possible. Data and analytics strategies need to be aligned so that there is a clear link between the way the organisation manages its data and how it gains business insights.

  • Include people from different departments who possess a cross section of skills: business, finance, marketing, customer service, IT, business intelligence, data science and statistics. Understand how these colleagues interact and what is important to them in terms of data outputs.
  • Take into account how data interconnects with your organisation’s daily business processes. This will help answer questions about the required data sources, connections, latency and inputs to your analytics strategy. Ensuring that they work together connects data to business value.
  • Finally, consider the technology components that are required. This entails looking at different platforms that deliver the required data access, data integration, data cleansing, storage and latency, to support your required business outcomes.

Measuring the benefits

The following organisations aligned their data and analytics strategies to deliver clear business outcomes:

  • Food for the Poor used high quality data and analytics to reach its fund raising target more quickly: reducing the time taken to raise $10 million from six months to six days, so that it could more quickly help people in dire need.
  • Lipari Foods integrated IoT, logistics and geo location data, enabling it to analyse supply chain operations so that it uses warehouse space more efficiently, allowing it to run an agile operation with a small team of people.
  • St Luke’s University Health Network mastered its data as part of its strategy to target specific households to make them aware of specialised medications, reaching 98 per cent uptake in one of its campaigns focused on thirty households. “Rather than getting mired in lengthy data integration and master data management (MDM) processes without any short-term benefits, stakeholders decided to focus on time-to-value by letting business priorities drive program deliverables,” explains Dan Foltz, program manager for the EDW and analytics implementation at St. Luke’s. “We simultaneously proceeded with data integration, data governance, and BI development to achieve our business objectives as part of a continuous flow. The business had new BI assets to meet their needs in a timely fashion, while the MDM initiative improved those assets and enabled progressively better analysis,” he adds. This approach allowed the St. Luke’s team to deliver value throughout the implementation.

These are just a few examples of organisations having a cohesive data strategy and analytics strategy which has enabled them to generate better value from   diverse and complex data sets.

Gaining better value from data

While analytics initiatives often begin with one or two clear business cases, it’s important to ensure that the overall data analytics strategy is bigger than any single initiative. Organisations that focus on individual projects may find that they have overlooked key data infrastructure requirements once they try to scale.

As Grace Auh, Business Intelligence and Decision Support manager at Markham Stouffville Hospital, observed during Information Builders’ Summit, “Are you connecting the dots? Or are you just collecting them?”

Capturing data in silos to serve tactical requirements diminishes the visibility and value that it can deliver to the whole organisation. The ultimate path to creating value is to align your data and analytic strategies to each other and most importantly to the overall strategy and execution of your organisation.

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