Order in Chaos

Big Data is ramping up to be a big business opportunity with companies on average doubling the amount of data they create every two years, and estimated to spend $120bn globally on data analytics software between now and 2015, according to IDC. Hype surrounding Big Data is at an all-time high as companies look to address the increasing volume, variety and velocity in electronic information. Traditional relational databases are becoming less useful and relevant as more ad hoc information is being created outside of those systems. But, while Business Intelligence (BI) tools have been around for a while to extract value from relational databases, few companies currently have the technology in place to apply the same degree of sophistication to unstructured data, such as call transcripts, documents, emails, instant messages and social media. Instead businesses have been hoarding this information, which is now spread across the business and stored or archived in numerous data repositories.

Although this information is easy to store in these repositories, the challenge lies in getting this information out quickly and easily. Extracting value from the information held in these repositories is usually very hard because it wasn’t stored in any uniform way and wasn’t categorised accurately. This is particularly pertinent as unstructured data contains a myriad of untapped and potentially valuable insight. But, unstructured data left spread across disparate repositories, unmanaged, inaccessible and un-analysed is worthless and represents a cost to the business in terms of storage. The ability to extract meaning from data is where its true value lies.

Unstructured data by its very nature is uncategorised and lacks the metadata that allows for easy identification and organisation. This can potentially leave data in a state of chaos, exposing the business to legal and compliance risks – especially those in regulated industries. This can end up costing businesses vast sums of money as they attempt to find and analyse information on a purely reactive basis. As such, organising, managing, and analysing this chaotic data proactively is a more cost effective approach, and can provide businesses with regulatory peace of mind and valuable insight through the evaluation of unstructured data. If businesses rely on structured information alone, they are potentially overlooking very important evidence and missing out on key information spread across their disparate systems within, as well as, conversations taking place between their customers and the business beyond its four walls.

Businesses need solutions that can help employees effectively access this key information. Integral to this is the ability to identify, understand and categorise the information amongst the vast amounts of unorganised and unstructured data. However, manual identification and categorisation simply doesn’t scale; therefore automation needs to be incorporated into the process to categorise these sorts of volumes.

By using software that accurately categorises any unstructured data in context, through the use of supervised learning and automatic categorisation technology, organisations can identify the relationships between entities, such as people, titles, instances, dates and departments. This software also provides a broad overview of the kind of information that resides on a company’s systems. This technology avoids the need to build a team of people to go through the information that businesses have stored on their repositories, map it out and find the relationships between entities. Instead you can use the experts within your company to teach the technology to focus on what matters to your business.

Businesses must embrace technology that can help them identify, categorise and manage unstructured data. By doing so, they can take advantage of the data explosion, rather than hiding from what could end up to be a Big Data minefield.

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