How Data Analytics
can Lead to Business Growth

With an assertive data analytics strategy, you can increase productivity, reduce costs, optimise processes, improve the customer experience, identify actionable insights and a whole lot more.

The standardisation of phenomena such as IoT (Internet of Things) has made interactions with websites, social networks or devices a source of information about their users. This knowledge has enormous potential to create business growth but without organising, studying and analysing this data, its usefulness is limited.

Which industries can benefit from the use of data analytics?

Most industries and business areas can benefit from implementing this. These days, data is a constant in every business: the healthcare industry collects data on patients, marketing companies analyse engagement patterns and the sports industry studies the performance of athletes.

In fact, it is estimated that 95% of businesses feel the need to manage their unstructured data.

Data collection, whether on a larger or smaller scale, is always present, but it is common for this data not to be organised or studied. Ultimately, this means there are opportunities to leverage data analytics across multiple industries.

At the same time, high access to user-friendly data analytics platforms mitigates the idea that only big businesses can benefit from big data analytics.

Data Analytics in the decision-making process

Decision-making is a constant process in the day-to-day life of organisations, but, despite being so common, it is a potentially time-consuming process with high inefficiencies.

As a business grows, decisions become more complex, and it is more complicated to make an effective choice that takes into account all the surrounding variables and scenarios. When several aspects of an organisation are affected, it is difficult to prioritise and choose the scenario that delivers the most value. In these situations, a thorough data analysis gains relevance, providing security in the analysis of different scenarios.

By leveraging big data analytics, it is possible to create a decision framework. This allows you to visualise the different areas of a business, understand the interconnection of processes and the possible consequences that may arise from a decision. The application of data analytics should be cross-departmental, assuming a prominent role in the management and growth of an organisation.

Benefits of adopting a big data analytics strategy

Data analysis has numerous benefits for organisations and consumers as it can be harnessed to achieve goals such as:

1. Cost control and efficiency

Whether in new companies or in established organisations, the cost structure is an essential factor in guaranteeing results and sustainability. Deciding how to allocate resources or manage investment in a project, taking into account business objectives, is a risky and error-prone process.

The use of algorithms and data analytics tools makes it possible to simultaneously study various scenarios, assess the risk of each one, and select the most cost-effective option.

As for efficiency, the constant collection of data in real-time makes it possible to visualise all kinds of operations, whether customer-related processes such as marketing or more technical processes such as production.

The capacity to analyse this enormous volume of data, in real-time, and to process this information makes it possible to identify the greatest efficiency losses and to reduce operating costs.

2. Customise and shape the customer experience

Many companies believe that a winning strategy is one that puts the customer at the centre of the decision-making process. The increased access to personal and customised information about customers and their experiences with an organisation has changed the way companies approach their consumers.

The application of big data analytics tools allows companies to visualise, in a timely manner, the points at which a specific customer interacts with business channels, as well as the possible pain points of that interaction.

This opens up the possibility to react immediately to the consumer’s journey, redirecting the customer to an experience more suitable to the moment of interaction and personalised to each individual.

For example, Netflix’s recommendation algorithm is highly customised to each user’s settings and tastes. To recommend a particular selection, the algorithm uses factors such as the time of day the user is interacting with Netflix, the device they are using, and the choice history of users with similar preferences.

The data collected about the customer journey allows us to react to the exact moment of use and ensures personalisation in order to achieve greater user satisfaction and retention.

3. Competitiveness strategy

With the rise of new companies, market competition is a factor that can limit an organisation’s growth. Differentiation is essential for organisations to survive in an increasingly specialised environment.

The use of data analytics can support the creation of an efficient competition strategy – capable of identifying innovative growth opportunities valued by customers.

Understanding consumer and competitor characteristics provided by data analytics systems can be the key to differentiation. By analysing consumers, it is possible to identify which characteristics lead them to select an organisation, and which factors direct them towards a competitor.

Additionally, it is possible to identify patterns in consumer behaviour, such as in which channels a customer gains knowledge about a brand, which products a typical consumer selects, and where the consumer buys complementary products or services.

This information is essential for an organisation to stand out from the competition as this data can be leveraged to make valuable consumer services available that are not being provided by competitors, or even create complementary product offerings that customers purchase from other competing businesses.

On the other hand, the analysis of data, both from the organisation itself and from competitors, can support the calculation of factors such as market saturation, the definition of a total addressable market, and the selection of markets with growth potential.

4. Anticipate problems and challenges

Delayed and inadequate reactions to unexpected problems or situations can be a major point of efficiency loss for an organisation. A company able to predict anomalies or changes in demand will be able to decrease these inefficiencies, and the costs of reacting to fluctuations in the market.

The large-scale collection and study of historical data makes it possible to create forecasts capable of identifying future process failures (such as the deterioration of a machine or a drop in demand).

It also enables the rapid identification of anomalies that might not be observable without mathematical methods, such as the movement of an inappropriate financial amount or an unforeseen drop in stocks.

It is also relevant to apply this forecasting capacity to cyclical situations – such as peak seasons. There are many organisations that market seasonal products or services. In these situations, if supply is not adequate to demand, non-compliance may increase, or, on the other hand, resources may be wasted. The use of big data enhances the forecasting of seasonal products, taking into account social factors, so that organisations can adapt their resources to the needs of their customers.

How to create a data-driven strategy

Creating a data-driven strategy is not a linear and equal process for all organisations, but there are good practices that should be followed for the strategy to be sustainable and appropriate.

• Foster a data analytics culture

Creating an analytics culture involves making data analysis the rule rather than the exception. To do this, organisations must adopt a disruptive culture that embraces new technologies, prioritises the use of analytics in all areas of the organisation, and communicates clearly what the relevance of using data analytics is and how it can improve the working conditions of employees.

• Demonstrate senior management alignment

Senior management sets the example that the organisation’s employees should follow. If a member of the senior team does not demonstrate that data analysis is a priority, employees will interpret it as secondary, and act on the message conveyed.

• Improve the processes that ensure data quality

The processes that ensure that data collection and analysis are correct and prioritised should be identified and adapted.

• Implement a FAIR data system

The data used by the company must clearly demonstrate four characteristics: Findability (data is easy to locate), Accessibility (data is stored and qualified users know how to access it), Interoperability (data can be used in multiple systems and be integrated with other data), Reusability (data is correct, reliable and can be replicated in other scenarios).

• Develop data literacy

For an organisation to be data-driven, all employees must have a culture of reading and interpreting data, and it is critical to ensure teams are trained in the use of tools to support data extraction and analysis.

• Understand that data analytics is carried out across the entire organisation and should not be perceived as an isolated department

Harnessing data analytics will only be possible if teams are aligned with a data-driven strategy appropriate to an organisation’s resources and objectives.

If the implementation of the data analytics culture is successful, it will be possible to grow in a disruptive way while ensuring the cross-cutting and binding role that data analytics has in an organisation.

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