Big Data Strategies for Agile Business

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Big data tools can empower businesses and allow them to operate with improved flexibility and agility. Of course, achieving your goals with the solutions available does involve adopting suitably impactful strategies, rather than simply expecting that big data adoption is enough on its own to turbocharge your operations.

With that in mind, here is a look at some of the tactics that businesses can deploy to make the most out of all that big data has to offer.

Embrace unified goals

This is one of the most important yet oft-overlooked aspects of entering the big data arena. In short, it is important for decision-makers to work together with those in IT to ensure that the aims of any big data strategy are aligned across all departments.

This will not only help avoid potential conflicts further down the line but will also allow collective conclusions to be drawn about what exactly the organization hopes to achieve from harnessing big data analytics.

A unified approach can then inform everything from the procurement of solutions to the performance tuning of the resources you adopt. If everyone is one the same page, you will be able to make optimal use of big data services.

Rank relevant objectives

It is likely that you will not have just one aim when formulating a big data strategy but will instead have a host of different goals in mind. Because of this, you might end up focusing on less pertinent objectives, which is why it makes sense to both define what you hope to achieve and work out which will bring the most benefits to your business.

Objectives can be broad, such as assessing large and varied data sets pulled from multiple sources in order to seek out correlations. They can also be narrow, such as analyzing mentions on social media to see how your brand is perceived by customers.

Prioritizing particular objectives can involve ROI calculations, although this is tricky in a big data context. Even so, having a rough idea of which ones are most deserving of your attention can allow you to remain agile and respond to changing market conditions.

Plot a route forward

Planning to use big data to make short term gains is all well and good, but you should also make sure that any strategy you develop also has relevance in the mid to long term.

Mapping out the kinds of tools you are going to be using and predicting how you expect them to influence the trajectory of your organization as a whole over the course of the next half-decade can be very useful in letting you see which aspects are going to deliver the most value over time.

As part of this process you can also lay out branching paths which will again contribute to the agility of your organization, accounting for differing eventualities and external pressures which might make one route more tenable than another in months or years to come.

Link data to procedural changes

It is all very well to adopt big data solutions to crunch the numbers and deliver insights, but unless you are willing to respond to and act upon the conclusions drawn, you will not be wringing the full value out of the tools at your disposal.

The key to business agility in a big data context is to put in place plans to change procedures according to insights gleaned from the information at your disposal. In short, the outcomes of analysis should be integrated with your decision-making regularly. Doing so will make your organization more responsive and less reliant on guesswork and intuition.

Experts agree that over time, a business which is fully attuned to its big data resources will become leaner and more agile, pinpointing weaknesses sooner and acting to address them quicker than competitors.

This is one of the reasons that big data can cut costs and has been proven to be effective both in larger operations as well as for smaller businesses.

As the market continues to expand, it is now more important than ever for firms to create big data strategies or else risk being left behind by their more agile competitors.

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Principal Program Manager at SentryOne