Achieving a customer first data strategy

Saurav Gupta at InterSystems explains why a customer-first data strategy is critical, and how to achieve it by simplifying data management

 

Customer-focus has become an essential aim for all enterprises. But too frequently, organisations embark on complex data strategies in the cloud without considering the business benefits.

 

This is a trend that businesses need to act on quickly. When they consider how to maximise all the masses of new data they have, they must put customer benefits first.

 

There is, in fact, little option to do otherwise. In today’s interconnected global economy, customer-focus is a fundamental requirement and no longer a differentiator.

 

Customer-centred data strategies enable organisations to respond with maximum effectiveness to shifts in demand and consumer behaviour. They can use multiple sources of data to achieve new levels of personalisation or to reshape and streamline their supply chains.

 

And with access to insights that tell them what customers want and how they use their products or services, companies can create new sources of value and revenue, and drive innovation.

 

The problem of data volume

 

The problem is that while AWS, Azure, Google and other cloud vendors have worked hard to make cloud migration quick and easy, enterprises still face difficulty in organising and extracting insight from the vast and ever-increasing amount of data they have there.

 

The challenge is only going to become greater. Gartner, for example, predicts worldwide spending on public cloud by end-users will increase by 18 per cent this year, hitting $305 billion in total. Next year the consultancy predicts the figure will be $362 billion.

 

Volume and complexity are inhibiting the implementation of customer-focused strategies. A survey of senior UK executives last year found more than half (51 per cent) were overwhelmed by the data within their organisation, rising to 61 per cent in larger companies with more than 1,000 employees.

 

The findings also revealed the difficulty so many companies have in processing, analysing and optimising the vast quantities of data they accrue on a daily basis. This multi-step process typically takes 76% of UK organisations a fortnight to complete. The process of analysing data for business decisions typically takes a week or two (41 per cent) or a couple of days (35 per cent).

 

However, 16 per cent of respondents disclosed that it takes them a month. Apart from eating up valuable time, it leaves organisations unable to make fast decisions to drive their business forward in rapidly evolving markets or changing economic conditions.

 

The demands of managing a large amount of data means enterprises spend too much time planning and collecting data without delivering any business value. To overcome this, organisations must rid themselves of their disjointed data architectures and simplify data management and preparation. This is how they can draft a new data strategy based on what customers want.

 

Organisations that are already ahead

 

In successful organisations, this his become such a priority that data strategy has reached board level. Boardrooms increasingly want to see their organisation decide what customers want and then shape and analyse their data to deliver it, rather than putting the data first and then decide their customer outcomes.

 

These front-runners have created specialist units to formulate their data strategy. They examine the data the organisation uses and how they store it and deploy it to improve services for customers and transform their operations. Data strategy is now part of business strategy and not just an off-shoot of IT. 

 

What a customer-first data strategy looks like

 

At the broadest level, a customer data strategy should focus on the twin goals of increasing top-line revenues by improving customer acquisition and loyalty and creating bottom-line value through operational efficiency and insights.

 

This is a potent combination that delivers tangible rewards. Customers are willing to pay a premium for products and services, boosting the bottom line. Customer-focused operations also improve customer experiences in myriad ways that reduce churn.

 

This requires companies to manage their data in a way that links insights very firmly to action. The connections between revenues and loyalty and between efficiency and value cannot be taken for granted, they must be measurable.

 

Achieving these improvements, and showing their impact on the business, requires use cases with measurable KPIs that explicitly tie data to insights, actions, and outcomes. However, making all these connections is extremely difficult when an operating model is fragmented and data architecture heavily disjointed.

 

What’s needed is a single, unifying strategy focused on driving greater value for customers, greater efficiency for operations, and greater impact for the business. Organisations should start by recognising that they do not need every byte of data.

 

More data is not only the answer to increasing customer focus. An effective customer data strategy will provide clarity, discipline, governance, and justification for what data is collected and how it is stored and used.

 

Even though they are migrating to the cloud and generating masses of new data, organisations need to simplify their architecture. Advances in database management have banished the old notion that simplification is at the cost of performance.

 

Simplified architecture is required

 

An enterprise data fabric is a new architectural approach that speeds and simplifies access to data assets across the entire business. It accesses, transforms, and harmonizes data from multiple sources, on demand, to make it usable and actionable for a wide variety of business applications.

 

It embeds a wide range of analytics capabilities, including data exploration, business intelligence, natural language processing, and machine learning directly within the fabric, making it faster and easier for organisations to gain new insights and power intelligent predictive and prescriptive services and applications.

 

By allowing existing legacy applications and data to remain in place, enterprise data fabrics enable organisations to maximise the value from their previous technology investments, including existing data lakes and data warehouses, without having to “rip-and-replace” any of their existing technology.

 

With analytics in place, fed by a streamlined enterprise data fabric, meaningful insights are delivered rapidly and in a format that is easy to grasp. This powers up the ability of specialist teams to build a customer-first strategy, implement and constantly update and innovate.

 

At the C-level, executives have fast access to data, insights and metrics that give them full confidence, enabling them to oversee their company’s continuing evolution into a customer-focused organisation.

 

Today’s data management platforms need to provide many of the critical capabilities – integration, data management, analytics, and API management – that are needed to implement an enterprise data fabric seamlessly. It reduces complexity, speeds development, accelerates time to value, simplifies maintenance and operations, and lowers total cost of ownership compared with implementing a data fabric using many different separate point solutions.

 

Simplification is the cornerstone of the customer-first data strategies that organisations moving their data into the cloud must have to achieve impact and maintain loyalty in today’s more dynamic and volatile conditions.

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Saurav Gupta is Sales Engineer at InterSystems

Main image courtesy of iStockPhoto.com