Building a data analytics strategy: offensive or defensive?

30 August 2022

Building a data analytics strategy: offensive or defensive?

Building an effective data analytics strategy is one of the most important steps for any company looking to take advantage of enhanced data-driven insights.

However, it can often be difficult to create the most optimised data analytics strategy for your business.

One such question that must be considered is the approach that your data strategy may take – will it take on a defensive or offensive form?

These approaches differ widely in how data is stored and used to give the company a key strategic advantage. What’s more, they can be a complex subject for users that don’t possess the technical proficiency to tackle.

Selecting between an offensive and defensive data analytics strategy can be a critical choice for enterprises, informing long-term use and investment. While these two approaches were regarded as non-cooperative, this may no longer be the case in an age of rapid innovation and accelerated capabilities.

What are offensive data strategies?

For businesses focusing on increasing their growth and brand awareness, offensive data analytics strategies may be the answer.

An offensive approach prioritises the use of analytics and data to explore greater revenue opportunities. Sales and marketing-centric analysis is a core example of this. Data-driven organisations will rely, in part, on offensive data approaches to support their business growth. As a result, these analytics will be more agile and up to date rather than their defensive alternatives.

Examples of offensive data activities include:

  • Using a real-time reporting dashboard to understand ongoing consumer trends
  • Leveraging big data analysis to explore routes that meet business goals
  • Deploying prescriptive analytics to source possible areas of growth

What is a defensive data strategy?

Unlike offensive data strategies that focus on leveraging data to pursue growth and development, defensive strategies prioritise minimising risk.

These risks may take on many forms, from ensuring compliance with data privacy regulations to enabling sufficient data quality and reliability.

At its core, a defensive data strategy is designed to enable consistently reliant and trusted intelligence. Due to this focus on minimising risks and prioritising security, enterprises in highly regulated industries (i.e. finance, law, healthcare) will usually focus on this type of data strategy.

Examples of defensive data strategy responsibilities include:

  • Ensuring compliance through rigid data management processes
  • Deploying descriptive analytics to detect and limit the likelihood of breaches
  • Implementing company-wide data governance
  • Understanding and identifying email open rates, website traffic, and sales performance

Offensive vs defensive: which is right?

Understanding which approach is best for your specific business goals depends on several factors – including your strategic plans and long-term targets.

To begin, consider the nature of your enterprise. Is it highly regulated and dependent on security – such as finance – or is it competitive and dependent on conversions and sales such as retail? Understanding how your industry traditionally regards and values data will set a good foundation for users to begin shaping their data analytics strategy.

As well as recognising the traditional use of data in your industry, understanding the short and long-term goals of your business can also help stakeholders choose between offensive or defensive strategies. If your organisation has previously ensured that they meet ongoing compliance standards, but are struggling to gain conversions and new customers, switching to a more offensive approach may be beneficial, and vice-versa.

It’s important to note that these two approaches aren’t mutually exclusive – as they were traditionally assumed to be. Certain industries such as insurance will value regulation, compliance, and attracting new customers equally – and the same may be true for many other organisations.

Balancing the two approaches

Balancing these two approaches in one data strategy is becoming increasingly realistic as the capabilities for data management and storage evolve – with innovations such as data warehouses and data lakes becoming more and more commonplace. Now, what was previously thought impossible and inefficient is achievable and streamlined.

Accessible and flexible cloud services can bring an agile approach to defensive risk management processes that mirror the clarity of offensive analytics dashboards. As a result, organisations can strike the perfect chord between offensive and defensive strategies to maximise the value of their architecture and data and reach their business goals.

Building your data strategy

Once businesses have identified and recognised the correct data analytics approach for them, developing a data strategy itself is the next step. This phase can be complex and time-consuming as it will dictate how teams store, use, and access vital data.

With each organisation’s architecture being unique and complete with its own set of strengths and weaknesses, building a data strategy that caters to your enterprise is essential. However, from overcoming the investment hurdle to estimating scalability, there are core challenges here that must be taken into account and resolved.

To learn more about the challenges of building a data strategy and how to get started, read our guide here.

Here to help

We’re passionate about helping enterprises from a wide range of industries elevate their data and architecture to meet their business goals. With a tried and tested methodology, we’ll work alongside you to understand your goals, examine your architecture, and implement a range of services – from building a unique data strategy to integrating Artificial Intelligence and Machine Learning solutions.

To discuss which data strategy is right for your company, and more, get in touch with us today.

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