What Big Data Can Do For Business – What To Gather & How To Benefit

March 6, 2019 Marcin Zima

Big Data is a popular buzzword in many boardrooms, but it is often understood on different levels. While some companies are trailing ahead with modern Cloud-based solutions, others are content to simply collect what information they have through traditional means.

However, there is a big difference between simply collecting information and making effective use out of it. Virtually all businesses, whether large, small, physical or digital in nature, stand to benefit from greater analytical insight.

This is why Big Data matters – it is both the acquisition and refinement of information at scale. Furthermore, if you’re business isn’t already taking advantage of what Big Data can do, you may already be behind the curve.

Why Big Data Is Important

Businesses have been collecting information long before this technology became available, so why is Big Data so vital?

Simply put, it’s a case of Big Data vs Small Data and the verdict is clear: the more information you have (and are able to effectively process), the more accurate the conclusions are. This, in turn, means that the insights gained, and the consequent decisions made, are more effective.

If you’re only assessing a fraction of your entire data, for example, how can you be sure that outlying data points are, in fact, outlying data points? It’s only when the entire information is analysed that this can truly be proved or disproved. In short, Big Data is better data.

It’s often stated that various inefficiencies can cost businesses anywhere between 20 to 30 percent of their annual revenue. Can your business state that it is 100% efficient and optimised? It’s a potentially big sum to simply throw away, so having the means to vitally assess every corner of your operations is essential if you want to trim excess costs and inefficient practices.

Another vital factor of Big Data, however, is that it offers near real-time results. Manual, human analysis takes a long time (and in large enough data sets can often be impossible). Because it’s essentially automated, Big Data can be generated as and when required so, rather than waiting on monthly or quarterly statistics, you can acquire the answers you need, when needed.

Through this, Big Data represents a means to uncover new growth opportunities, such as previously undiscovered trends that can then be implemented as part of your ongoing strategy. Big Data can help you learn more about your customers, create better products, optimise your processes and even anticipate user behaviour.

Why Big Data Is The New Competitive Advantage

The biggest reason Big Data has become a hot topic over the last few years is because the technology has become much more available and affordable.

Previously, companies had to invest in ongoing storage solutions for their data, as well as expensive analytical tools and software to generate the data. This resulted in high expenses for hardware, custom software, ongoing maintenance and the personal needed to run everything. Now, companies can simply combine Big Data with Cloud computing to eliminate these barriers. The latter allows for remote data storage at minimal cost, while additional serverless solutions can allow you to generate reports and insights when required, only paying for the processing power used.

Additionally, being competitive also involves making vital decisions as quickly as possible. Since such choices still need to be fully informed, Big Data gives companies a means to adapt on the fly – to react a greater speed than ever before.

This can also include minor results. Big Data can uncover smaller peak trends that typical analysis might not find. In turn, businesses can make minor adjustments that increase profits or enable their business to remain competitive.

Finally, because Big Data is becoming more readily available, it can be found across all industries and organisations. In turn, this means that those that aren’t already using it stand to lose out to competitors. After all, if they’re able to uncover in-depth findings and trends at both a faster and larger rate, it’s an uphill struggle to compete without doing the same.

Big Data And Business Intelligence

One of the biggest presumptions about Big Data is that it is in opposition to Business Intelligence. In reality, Big Data serves to empower and enable the latter.

Both Big Data and Business Intelligence seek to take large datasets and use them to benefit the company, whether it’s through expansion, streamlining processes or otherwise optimising costs. Yet the latter is all about researching areas where you expect to find results, where the former is also able to explore the unknown.

Another key difference, however, is that Business Intelligence traditionally relies on offline Data Warehouses, using datasets from specific time frames and periods. These warehouses require clearly structured data right from the start, which adds limitations. Big Data is, in many ways, a modern evolution of this aspect. Unlike data warehouses, this more modern solution utilises data lakes, which can analyse semi-structured or even unstructured data to perform a broader range of analyses. Consequently, data can be assessed in real time, while businesses still retain the ability to create seasonal or periodical reports, as required. This also includes historical data (regardless of how its structured). If you have the information, Big Data can – and will – make use of it.

Big Data Clouds also remove the need for these offline servers: instead, you can keep the data in the Cloud to generate reports and findings as required through serverless data solutions. For many smaller companies and enterprises, the ability to manage Big Data solutions without the consistent, expensive overhead costs of hardware management have made these possibilities a reality, rather than a potential future prospect.

Big Data Opportunities And Use Cases

One of the biggest hurdles with Big Data is knowing what information to track. Having access to every piece of data at once is a huge benefit, but companies still need to have intended goals and outcomes. Knowing what you want to improve, however, will readily reinforce which data you should prioritise.

So, what Big Data opportunities are there? Depending on the exact nature of your industry, quite a lot…

Anticipate & Predict

The more you know about customer behaviour, the more you can predict certain trends. A classic example of this can be found in online retail. Numerous shops will offer customers recommendations based on what they’ve just purchased. This only works because Big Data looks at all previous customers and finds the next products they are most likely to purchase.

Such recommendation options are very common today. You can see it on Amazon, Netflix, Spotify, and countless other services – and it’s all thanks to large scale data analysis.

Furthermore, Big Data offers the opportunity to compare with any other available data. For example, are your customers influenced by external factors, such as the weather? Once you identify a correlation between a seemingly arbitrary point and an sharp change in customer behaviour, you can start to anticipate, expect, and prepare for these fluctuations.

Potential data to consider:

  • Customer shopping history – what items do customers buy most frequently, and is there any statistically relevant correlation between specific items?
  • Categories of items viewed/purchased – Do people who purchase specific categories strongly overlap with another category all together?
  • User journey maps – how do users navigate through your services? Does purchasing or investing in one area cause them to consider other aspects of your business?

Behavioural Influences

Similarly, there are many aspects that influence customer behaviour, as well as other internal factors. For instance, do your products sell better in different temperatures or seasons? If so, analysing weather data alongside your own data can offer additional insight – we created such a solution for this with Amazon Athena and Big Data visualisation.

Potential data to consider:

  • Time of interaction – what hours see the most activity? What about days of the week, months or seasons?
  • Weather – are people more active when its warmer, or does a drop in temperature bring its own changes?
  • Location – which areas and items/services have stronger correlation? This can also be combined with other factors, such as weather, to give a greater view of how your business is used by customers.


When you combine predictive customer behaviour and current behavioural influences, as discussed above, you are able to offer the most personal service possible. Not only are you aware of current user trends (what users are most likely to use, purchase etc), you also understand how they react to other influences.

Using historical data, you can assess future periods that match, offering a personalised experience to meet these needs at just the right moment. Shops can offer summer clothing during the holiday season – but only if and when it’s warm enough – while logistics firms can anticipate large orders and ensure transportation will be available.

Potential data to consider:

  • Customer shopping history – what previous items have users purchased? How strong is the correlation?
  • Categories of items viewed/purchased – do customers in one particular area have a strong overlapping interest in any others?
  • User journey maps – how do customers navigate through your range? Do they all start with a common need before using your additional services/products for more niche demands?
  • Time of interaction – what hours see the most activity? Do sales of certain items or services change during the week, months or seasons?
  • Weather – are certain people more active when its warmer or colder and can this be used to anticipate their needs?

Better Designs, Products & Services

Every company wants to make its products and services better. However, to better design these, you need data on the existing iterations to understand what can be improved. What features are lacking, and what features could benefit how users directly utilise such services?

Potential data to consider:

  • Time spent on various features/services – which features are used the most? Are people spending too long on particular services or not enough?
  • Frequency of activity – how frequently do customers return and which areas see the most activity?
  • User journey maps – how do customers navigate through your range? Do they all start with a common need before using your additional services/products for more niche demands?
  • Sensor & input data – if you have sensors or record input (for example, on various vehicles or electronic products) you can learn more about how devices are actively used. Automatic car manufactures such as Tesla use Big Data like this, using the sensors on their vehicles to better improve their vehicles driverless systems.

Optimised For Efficiency

The Theory of Constraints teaches that there is always at least one constraint in any large process and, in this regard, most businesses understand that they can only be as efficient as their weakest point. It stands to reason that uncovering weak links, improving them and repeating the process indefinitely will result in a cycle of continual improvement.

There are countless sectors where this can apply, but one of the biggest areas of improvement lies in introducing Big Data to supply chains and manufacturing processes.

Potential data to consider:

  • Performance data – how much can each warehouse handle at peak productivity? How quickly can your production chain produce finished goods and what other factors impact this?
  • Cycle times – how quickly do your supply chains, delivery cycles etc loop?
  • Delivery times – when are deliveries made? Are they quicker at certain times – for example, during the week or during off-peak traffic periods?
  • User journey maps – are users taking unnecessary steps to get where they need to in your app or service? Perhaps customers have to go through too many steps to speak to the right people in your company. It’s worth checking that such processes aren’t causing people to abandon the service or look elsewhere.
  • Sensor & input data – Sensory data and any extra input can show how products are used and if there’s any room for efficiency. If your transport operations are stopping and starting, this can waste money in fuel, for example, whereas a more optimised route can resolve this issue.
  • Inventory figures – How much inventory is there? When do certain items deplete? Combined with additional data, there are plenty of ways to improve efficiency. Can you cut down on warehousing, while still ensuring in-demand items always have available stock? Can you combine restocking efforts to cut down on logistics? The possibilities are endless.

Pricing For Profits

Is it better to sell 10,000 items at a 10% profit, or 5,000 at a 25% profit? If you’ve been selling services and items for a long time, there is a wealth of information available to determine this answer. In other words, you can find the most ideal price points to better position yourself within your respective market.

Potential data to consider:

  • Price values – which prices make the most sales?
  • Profit margins – how much profit does each sale make? Multiplying this by the volume it sells for at this price can reveal some interesting conclusions.
  • Sales figures – of course, you need sales figures for all of this to work.
  • Order fulfillment rates – how well are you meeting customer orders and demands? If you’re not able to meet peak seasons, then higher sales volumes might prove difficult.

What About Big Data And Machine Learning?

We’ve discussed Big Data in terms of what to collect, but what about the next steps? Automated data collection at this scale works best with automated analysis, and this is where Machine Learning (ML) can prove very beneficial.

Machine Learning works exceptionally well with large data sets, drawing its own conclusions, refining processes and adding to the data in a form of continual improvement. Programs that can learn from their own results will continually develop, adding new data to historical information for the most accurate results.

One key example of this is financial fraud. Numerous banks and financial companies are using ML to detect fraud, based on patterns of previous behaviour across a wide range of metrics (location, typical transactions, type of purchase etc) with increasingly better success rates. Yet all of this requires large volumes of data to determine the trends and statistics that such prediction methods require. This means you still need some form of storing and collecting Big Data to empower ML solutions.

If Big Data Is The Future, What About The Human Element?

The secret to Big Data is that it only replaces human input at a menial level. Humans are still needed to provide creativity and innovative answers.

There are some decisions that Machine Learning or Artificial Intelligence (AI) won’t be able to make. For instance, creating new strategies, adapting to larger changes and global trends still require human innovation. Big Data can find the trends, but human input will still be needed to act on them. The benefit, however, is that such specialists can now dedicate more time and resources to creating new strategies and solutions, as time is now freed up from simple data collection and manual analysis.

Additionally, don’t forget – as stated earlier – you can still use Big Data to accomplish Business Intelligence tasks. If anything, Big Data will make these departments more efficient, offering more up to date information – your teams won’t be making predictions and analysis on outdated information anymore.

Getting Started

Thanks to the powerful combination of Big Data and Cloud computing, gaining insights, discovering new opportunities for innovation, or simply solving previously unnoticed problems are all readily available for businesses. If you’re not already making the most of Big Data solutions, it’s quite easy to get started:

  • If you’re currently not utilising any sort of data analysis or intelligence, creating a Cloud-based solution will give you an affordable place to store data, pulled from various parts of your business and easily accessible when required.
  • Next – or if you are already collecting some information – Big Data is as simple as collecting this information and utilising serverless functions to analyse and create reports. This is ideal if you want to find specific information. At a more advanced level, Machine Learning can be utilised to find and make recommendations on its own.
  • If you already have a Business Intelligence department or team established, Big Data can be the next step for them. With Big Data on their side, analysts can make the reports they always wanted, but lacked the manual hours to do so.

No matter which stage you are at, it’s vital to have a solution that’s lean and cost effective: while there are many Big Data uses in business, there’s no point solving inefficiencies in your operations if the answer costs more than the problem. Thankfully, utilising Cloud technologies to both store and process data minimises the costs that previously made such solutions unaccessible to all but the largest enterprises.

Business Perspective

All businesses, large or small, generate data but not all take advantage of it. Big Data can find new conclusions from existing, historical information, or even correlate your internal data with external information – all of which helps organisations to discover new streamlined solutions and improved efficiency.