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The role of big data management in organisational decision-making

Due to the sheer size and volume involved most enterprises find big data management a challenge Here's how you can manage it better!

With the emergence and subsequent dominance of the internet, the amount of touchpoints businesses have with their customers has multiplied considerably. Social media, websites, blogs, forums, mobile devices – the list goes on and on. On these platforms, a gargantuan amount of data is created every day. If this data is properly stored and analysed, it could provide organisations with invaluable data regarding user behavioural patterns, preferences and even insights into their competitors. However, due to the sheer size and volume involved, big data management has been a challenge for most enterprises within the last decade – that fact has changed over the past few years.

With the introduction of new applications and techniques like cloud management, an increasing number of businesses have embraced big data. As a result, big data management and the insights it delivers have become the basis for many organisational processes, including decision making.

Using big data analytics for organisational decision making

To start off with, it’s important to understand how big data analytics is utilised for decision making. While from the offset it may seem like a mystical process, the collection of big data analytics and their utilisation in decision making isn’t all that complicated.

Goals are identified by the business initially. These will be the benchmarks you use to test performance and identify whether the business is heading in the right direction. Once the goals and performance metrics are identified, it’s good practice to refine them. This ensures that only the best data is collected and that your analysis is ultimately better.

Following this, the most important step in big data management occurs – the data collection. The goal here is to use as many relevant sources as possible; as we said earlier, with the abundance of customer touchpoints, this shouldn’t be an issue. Data compiled can either be structured or unstructured and it will be up to the software you’re using to make sense of all this.

All collected data should subsequently be refined, and be categorised based on their importance for achieving the goals identified earlier. After unnecessary data is weeded out, it’s imperative to segregate everything based on what their purpose will be – is this going to help improve efficiency? Will this help improve consumer relations? And so on.

Once the data has been prepped it’s time to start analysing and applying. Here it’s imperative to choose the right tools and software for your big data management, as they can reap great benefits for your organisation. And now you’ll have your valuable insights, meaning you’ll be ready to execute strategies and make decisions based on them.

So, with everything set for you to start utilising big data in the decision-making process, what’s next?

Building better consumer relationships with big data management

For most organisations, the crux of their operations revolves around the relationship they maintain with their consumers. Strengthening and building upon it often serve as the key to a business’s successes. It’s a pretty simple equation – the more engaged your customers are with your product and brand, the better your conversion rate is going to be. This simple fact makes the goal of customer-related decisions relatively straightforward – ensure they are engaged and that you retain them.

Big data management provides the opportunity to do just that. Effectively utilising big data reveals previously unidentified trends and patterns about your consumers. This includes their buying patterns, product partialities and even the relationships they have with your competitors. With this information in hand, organisations can begin crafting tailored content – from product launches to full-blown marketing campaigns – for your consumer base.

Boosting operational efficiency with big data management

All organisations strive to be more efficient. Decisions are always being made with the goal of improving performance in both the workforce and in everyday processes. The issue is, it’s not always inherently clear what the best choices are; it isn’t uncommon for organisations to resort to trial and error to identify the best practices. Big data is able to demystify all of this, however. With big data management, the outcome of efficiency-related business decisions can be calculated fairly precisely on a real-time basis.

Automation has also become a preferred option for many businesses looking to improve their efficiency. This even includes automating the decision-making process itself – and this is a data-driven affair. By melding big data with automation software, organisations can create a system that streamlines the decision making process and subsequently boosts work efficiency.

Access to increased capacity without extra investment

Companies always have a plan to grow; to expand their services, grow their consumer base and raise their brand image. The decision-making quandary with expansion is the investment that it requires. Once again, big data management alleviates this issue. Think of all the optimisation possibilities that are uncovered with effective utilisation of big data. Now add all the consumer engagement and retention opportunities it delivers. Simply put, decision-making brought about by the real-time analysis of data will create natural growth for your business, with no need for any additional investment.

As such, the role big data management plays in the organisation decision-making process is apparent – it’s a vital tool that eases the pressure and doubt that surround major business decisions. Effectively using big data when making decisions is near-guaranteed way to build better relationships, foster a better work environment and facilitate healthy growth for an organisation.

Formulating the ideal data strategy

Businesses need a comprehensive data strategy to properly integrate analytics into their operations. In this blog post we discuss this.

For the past few months, we have been espousing the benefits of data analytics for different industries like aviation, healthcare and marketing. But, are organisations making the most of their data analytics? Research shows that while there is some progress, there is still much work to be done in incorporating data into company operations. One reason for this lack of integration is the lack of planning from the organisation. Put simply, organisations need a thorough, comprehensive data strategy to properly integrate analytics into their operations. In this blog post, we take a look at how to formulate a winning data strategy and how to implement it.

What do we mean by data strategy?

First, what do we mean by data strategy? It is a multifaceted plan that covers how organisations will use data in company operations. It is not just about identifying key metrics or creating reports. It is also about integrating data into different facets of the company. A data strategy provides insight into how organisations look at their data analytics capabilities.

A comprehensive data strategy helps businesses understand what their data can do for them, why certain trends are happening and how to optimise these trends to increase revenue. These strategies are usually endorsed by high-level management because they provide a new direction for the organisation.

What are the essential factors of a data strategy?

To formulate a strong data strategy, organisations need to devise a strategy that is closely aligned with their overall strategy, must be flexible and have the ability to assess different aspects of the organisation. Strategies must be tied to organisational goals because they have to potentially transform the organisation’s business model, increase profit levels and drive growth.

A well-designed data strategy does not focus on one or two KPIs – it covers different dimensions related to security, compliance and governance. It allows organisations to make the most of business intelligence software, and pave the way for the future. Furthermore, data strategies must be nimble and flexible, so that they can be periodically reviewed to accommodate changes in the market, and within the business. While this is by no means a comprehensive list, the best data strategists clearly define their strategy in the following areas: data content, security, licensing, internal communication, quality and performance.

How is the strategy implemented?

Some organisations create a detailed data strategy, but often do not implement these plans due to a lack of planning. Hence, implementation is also a huge part of strategy formation. So how does one create a suitable method for implementing the strategy? By detailing a roadmap. A roadmap is a commonly used term in the world of IT – it is used by developers to show customers what new features and updates are coming down the road. In the world of data analytics, a roadmap means turning the strategy into an actionable plan. An analytics roadmap creates this kind of framework and allows organisations to evaluate the value of strategic initiatives. With an analytics roadmap, organisations are forced to look into how each day goes into implementing the strategy.

The roadmap allows the organisation to convert abstract goals into skills needed, types of people to hire and a specific timeframe. A roadmap converts large structure altering goals into actionable bite-sized activities. Every organisation needs to have a roadmap because data analytics is a technology that requires the entire organisation to be mobilised, and the roadmap helps in this matter. To create a data strategy, organisations need to work with a third-party expert – working with such an expert means a pair of fresh eyes and a more objective opinion on current practices. It is also just as important to get a third-party who is an expert in data analytics to provide the best data strategy available.

Moving forward with data analytics

A data strategy is vital for converting data analytics into a business asset and, without a proper plan, it is impossible to take the next step in analytics and that’s working with data analytics specialists. Such specialists can provide several helpful services like administration, hosting and installation – helping firms take their data analytics strategy to the next level. The right group of consultants can help make the strategy and roadmap a reality, at a smoother, more efficient process than ever before.

At the end of the day, it is important to have the right data strategy in place, otherwise, organisations will not be able to realise the full benefits of their data analytics platforms.