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How Big Data and Machine Learning can Impact your Business

Machine Learning and Big Data are in the spotlight and are reshaping the way industries use data

Big Data and Machine Learning are bringing a new reality into business in general, they are reshaping the way companies use data and their upcoming plans. Even though, for some businesses it might sound something too far away, they are more realistic than we believe, and it can help enterprises gain a competitive edge. By 2020 it’s expected that the accumulated volume of big data will equal 44 trillion GB.

When it comes to Big Data the first big chance is how it can impact business decisions and investments. By analysing data and crossing it from different sources, companies now can develop products based on what their customers need or change plans faster in order to adapt to a specific situation. Strategic decisions can be made precisely, obviously affecting the results and increasing the chances of success. Marketing and sales, product development, business strategy are just some of the departments highly affected by the proper use of all the data that the world is collecting. 

Big Data Fours Vs

We gathered below the most important topics to be considered regarding to Data. If your company is considering the use of data intelligence, it’s recommended the understanding of the 4 “Vs” below. You might believe that you don’t have enough sources, and you might find out that you have them in house.

Volume – Scale of Data

Organisations can collect massive amounts of data from various sources, from business transactions to social media and machine-to-machine data. There are loads of data available on the web, from Google, social media, and even governments are making their data available in order to be more transparent. It’s just a matter of collecting it, select it, organise, and transform in a management report.

Velocity – Analysis of Streaming Data

Data streams in at an unprecedented speed and must be processed and analysed in a timely matter.In the digital era to act as fast as possible, it is essential. Once the data are collected, it needs to be analysed soon to be a useful resource for the enterprise and help in the decision making process.   

Variety – Different Forms of Data

Data comes in all different formats, from numeric data in traditional databases with unstructured data such as text documents, video, audio and email. It means that the analytics part, it’s essential to gather all this information and convert it in a usable resource.

Veracity – Trustworthiness of Data 

There is no guarantee the data you collect will be clean and accurate. Organisations need to keep data consolidate and cleansed to extract the right insights. The original source is crucial and to know where the data comes from as well.

What Industries Benefit from Machine Learning

Most industries that work with large amounts of data recognise the value of machine learning technology. By gleaning insights from big data in real time, organisations can work more efficiently, cut cost and gain competitive edge. According to a Forbes article, 6 out of 10 organisations reported that Machine Learning or AI is their most significant data initiative for 2019.

Industries such as financial services, government, marketing and sales, healthcare, energy and utilities, transportation and more can benefit from Machine Learning technologies, not just to predict their customer’s behaviour, but also to automatize actions.

Healthcare systems are a good example of how Machine Learning can help and can be used for good. The amount of data collected in China from patients, for example, it’s already enough to help doctors in training to identify common diseases in the hospitals. In the last measure AI technology performed more accurate them the doctors in training.

Some examples of Machine Learning use:

  • Personalised content and product recommendations
  • Automated customer service agents
  • Security intelligence
  • Automated preventive maintenance
  • Fraud detection
  • Predictions and modelling

How to Implement a Data Analytics Strategy

Let's imagine that your company possesses the data, see the value of it, but how to implement a data-driven strategy? Even though, the immediate value that an analytics strategy can bring to the companies, it can be difficult to implement such changes. According to “The Analytics Store”, New Horizons Ireland partner for Analytics courses, it’s necessary to embrace a questioning culture and move from strategy to decision implementation.

Having said that, is important to highlight that Analytics first need a business strategy. Analytics will deliver insights to be actioned by the organisation, that’s why the first step is to have a clear, well defined, well understood and actionable business strategy. Examining the business strategy will allow to identify concrete opportunities where analytics can help the enterprise to achieve their goals.

One more point featured by the experts from The Analytics Store is to avoid an analytics implementation just because is a trend. When it’s performed just because it is fashionable, there are only two likely outcomes and they aren’t positive:

  • Discover solution for problems that aren’t critical. While the insights might be interesting, they don’t have a huge impact in the business and might be not tangible to use.
  • No solution will be discovered at all.

In both cases you will have the costs involved and won’t have an immediate opportunity. The recommendation is always to have a well aligned business strategy, which will impact all departments and convert the analytics results in more businesses and profit to the company.

The Challenges Ahead

Big Data and Machine Learning initiatives are bringing a new reality to business and they will face a new set of challenges in the AI era. Handling large amounts of data is one of them. Managing the sudden influx of big data and new data formats is a challenge. To deal with data growth, businesses are turning to tools like NoSQL databases, Hadoop, Spark and other BI applications to help them come through big data stores and extract the insights they need.

Talent is another area affected and some shortage might be expected. Finding skilled professionals with the technical ability to understand and implement machine learning is not easy. Facing high demand and low supply, businesses are struggling to hire qualified data scientists and data analyst and meet salary expectations. Training current employees on big data concepts and technologies can help offset the skill gap.

Data Security is a challenge as well, as it comes from a wide range of sources, making security and compliance difficult to manage. To protect big data stores, companies should focus on strengthening security measures, such as to identify and access control, data encryption and data segregation.

*The above article is based on the information provided by "The Analytics Store" blog and data from New Horizons Corporate

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