There’s a lot of misunderstanding about how Big Data analysis can be used, and the companies it can be used by.
One of the main responsibilities of the FD or CFO is to manage risk — financial or otherwise, but there’s a problem. The vast majority of organisational risk, these days, doesn’t sit in closed fields such as spreadsheets or databases (‘structured’ data).
Instead, it’s found in unstructured data, which might include: reports; contracts; invoices; correspondence; emails; instant messages; voice files; social media posts; video content; PDFs and paper.
In fact, it’s been estimated that around 80 per cent of business-relevant information now originates in unstructured data. Unfortunately for FDs, it’s exactly this kind of data that is the toughest to analyse.
The challenge of silos
There’s another problem, too: silos. Today’s companies, especially larger ones, are often siloed into multiple divisions, which means communicating with one another is often challenging. The analysis of vast data ecosystems is hard enough in itself, but once a company fragments divisionally, communication tends to breakdown, or at the very least suffer.
It therefore becomes even harder to spot and contain new company-wide risks and threats. Organisations effectively become opaque. Thankfully, that’s where Big Data can help.
Enter Enterprise Big Data
Today’s Enterprise Big Data companies, despite the impression given in the media, aren’t just there to tell marketers that a teenage girl is pregnant so they can send her free nappies in the post (true story). They’re also there to help companies de-risk and uncover the threats, financial, criminal, reputational or otherwise, that are hidden in plain sight.
But enough talk. Let’s get down to the practicalities. In what ways can Big Data analysis be used to protect against risk for the average FD? The answer is lots. We can’t cover them all here, but Enterprise Big Data can be used, for example, to: uncover fraudulent or other criminal activities; highlight regulatory risks and compliance oversights; discover new revenue opportunities; expose corporate, or M&A, risk; and deliver substantial cost savings.
But even that’s a bit wooly, so here are some more detailed examples:
• Acquisition due diligence. Enterprise Big Data platforms automate and rapidly speed up the process of collecting all structured and unstructured data relevant to a possible acquisition, from accounts, contracts and databases to people, policies and procedures. The targeted firm’s internal data can also be linked with external sources of data including company filings, analyst reports and social media. In effect, Enterprise Big Data can provide a level of corporate due diligence that would be impossible through manual procedures.
• Market intelligence. Enterprise Big Data platforms make it far easier to gather intelligence about customers across vast (and even heavily siloed) data ecosystems. They are able to make connections where, to the manual observer, there are none. As a result, companies globally can gain powerful new insights into their customer base, enabling data-driven personalisation, price elasticity and empowering them to respond to new market trends as they emerge. Enterprise Big Data can effectively future-proof companies.
• Fraudulent activities. Once again, Enterprise Big Data platforms automate the process of collecting and connecting transactional data, and enrich it with data from relevant external sources, such as known suspicious activities. Applying aggregated risk weightings and probability analysis, it effortlessly identifies highly suspect transactions and provides a full audit trail for use in any subsequent investigations or legal activity. Combating fraudulent claims and avoiding bad business have already made significant advances using Big Data to protect against financial crime.
• Anti-money laundering. For financial services firms, Enterprise Big Data platforms are able to connect data across the often unbridgeable Know Your Customer (KYC), Customer Due Diligence (CDD) and Enhanced Due Diligence (EDD) environments. Where necessary, the data is extended to include sanctions, political exposed persons and other data related to risk. Big Data is being used to highlight irregularities and to protect financial companies from regulatory investigations and penalties.
As someone on the inside of the Enterprise Big Data industry, there’s a lot of misunderstanding about how Big Data analysis can be used, and the companies it can be used by.
It’s not just about connecting people to products and vice versa, and ramping sales. In reality, it can be used by any company from any sector that wants to learn from both its internal, and relevant external, data.
And crucially, it massively collapses the time between an investigation or piece of research being carried out, and the results — new intelligence, suspicious activity — coming in.
Best of all, perhaps, today’s Enterprise Big Data platforms are cloud-based, which means they’re cost-effective and collapse the time to value.
The secrets within data are no longer the preserve of the very biggest companies but are available to one and all.