– yet research shows slow progress up the data maturity scale
More than three-quarters (77%) of businesses now rely on data for planning and forecasting, according to an MHR Analytics survey.
Yet progress along the data maturity scale is slow, with initial findings from the Data Maturity Quiz indicating that most businesses are still stuck in the early stages.
The quiz is a diagnostic tool that assesses the extent to which organisations are optimising their data to achieve goals. It provides guidance on overcoming barriers to climbing the data maturity scale and has so far been completed by over 150 medium-to-large organisations.
Findings from those completing the quiz to-date show 68% are now fully committed to harnessing analytics to make evidence-based business decisions. But nearly half (43%) of respondents are struggling to determine what big data initiatives to measure, and 60 percent don’t have a strategy to improve the level of insight from their data.
Two thirds (66%) remain in stages one and two (the operational and descriptive stages) of the five-stage scale:
STAGE 1 – Operational. Reporting is limited to tasks that are critical for business operations, with no formal BI (Business Intelligence) and analytics tools or standard in place to support this, and spreadsheets used as a primary means of reporting.
STAGE 2 – Descriptive. BI and analytics are in their early stages of implementation and are used to report on activity.
STAGE 3 – Planning. Using tools like scenario planning, BI and analytics are used not just to report on what’s happening, but to plan for the future.
STAGE 4 – Predictive. Data analytics is used to predict what will happen five, ten, even twenty years from now and to pinpoint the key drivers of trends.
STAGE 5 – Prescriptive. Users no longer have to input variables into the system to predict future outcomes. Instead, Machine Learning and AI make it possible to detect issues before they’re even considered.
“Many surveys show organisations are well aware of the benefits of the planning and predictive capabilities they couldbe using, particularly within finance and accounting functions, but most are not adopting them yet, despite evidence that those who progress further along the journey soon reap the rewards,” said data maturity expert Laura Timms, product strategy manager at MHR Analytics.
“It is often data quality issues that prevent businesses from climbing the data scale as quickly as they would like, and that is where investment should typically be focused – in collating and coordinating existing data sets to enable much more valuable systems to be implemented. A recent PwC survey showed the main *challenges to implementation for 62 percent of businesses were data silos or organisational silos.”
“The results mirror what we see when working with our 750 customers around the UK as well as overseas. Spreadsheets and the manual work that comes with managing them are still blighting data quality and business processes, making it difficult for organisations to break down silos and obtain a clear picture across their business and progress to the further stages of data maturity they need to compete.”
The MHR Analytics Data Maturity Quiz produces practical steps to improving data maturity and aims to demystify some of the jargon about data and provide a no-nonsense diagnosis.
STOP THE CONFUSION: HOW TO KNOW IF YOUR BUSINESS MAY BE INSURED AGAINST COVID-19
By Alex Balcombe, Partner at Harris Balcombe
The last few weeks has seen businesses in hospitality, tourism, retail, leisure and more forced to close their doors following the Government’s orders that they should close to prevent the spread of coronavirus.
While this is expected to flatten the curve and reduce the number of coronavirus cases, it will of course have an impact on businesses and employees alike. For small businesses especially, there are many concerns about how they can claim on their insurance to weigh the fall of this impact.
In response to calls to help struggling businesses, the Government has informed the public that companies who are facing turmoil will be able to claim on their business interruption insurance during this difficult time. For most, this is wrong.
The insurance industry has also been extremely vocal that there is no cover for any coronavirus-hit businesses during this tough financial period. This isn’t strictly true either.
How can businesses see through the mixed messaging and best secure their future and their livelihoods and reduce money worries? It’s an extremely stressful time for many companies, and confusion over whether or not they can be covered can only cause more unnecessary stress.
Since it’s a new disease, most businesses will not be covered for business interruption due to COVID-19. In fact, the vast majority of policies do not cover anything related to COVID-19.
That said – don’t rule out the idea that you may be covered. There is a chance that you will be covered against COVID-19, but not know it. This is a very small chance, but your current cover may already protect your business against the consequences of coronavirus, and the nationwide response to it – though those with this cover are unlikely to realise it.
How Could I Be Covered?
Not everyone has business interruption insurance, as it’s not a legal requirement. It is entirely up to the policy holder to weigh up the benefits of having it, and their ability to trade should a disaster happen.
To be considered for cover for COVID-19, there are two types of policy extensions to your business interruption cover that can potentially cover you for this situation:
Infectious Disease Extension
Many policies expressly state which diseases fall within the realm of being an infectious or notifiable disease. If this is the case, your policy will not provide cover. As it is a new disease, these policies will not have included COVID-19.
Other infectious disease extension policies will define the disease with reference to the actions of the government. Since the UK Government has named COVID-19 as a notifiable disease throughout the UK, it is possible that your business may fall into this definition, thus meaning you may be able to make a claim.
However, again, it’s not always that simple. Many policies require the disease to have been on your premises, while others specify a radius from your premises in order to qualify.
Denial of Access Extension (non-damage)
Denial of Access Extension (non-damage) policies may cover you if you’re prevented from accessing your property. This could be due to an event, or by the actions of a competent authority, which could cause your business interruption cover to engage.
If covered by this clause, there are often very subtle differences in wording in your policy. This could depend on the insurer or policy. You may well be covered, but it will depend on your particular circumstances, and the specific policy wording.
It’s clear that the Government needs to do more in ensuring there is clear messaging for businesses, and to help the insurance market look after policy holders. This is an unprecedented situation, and with many people looking to claim on their insurance, we’re already seeing major delays which could have a domino impact.
People throughout the world are understandably facing all kinds of worries because of the current pandemic. Our ways of living have changed, and many business owners will not have experienced a situation like this in their life times. If you own a business and are unsure about whether you can claim for business interruption, or are confused about ambiguous wording, get in touch with a loss assessor.
These claims are not simple, but loss assessors will be experts in business interruption insurance, and will specialise in large and complex claims. They will be able to help and guide you along the way, check your wording and work on your behalf to make sure you get everything you are entitled to.
HARNESSING ANALYTICS IN THE FIGHT AGAINST FRAUD
By Anna Lykourina, EMEA Fraud Analytics Expert at SAS
In the past, the fight against fraud has been a bit hit-and-miss. It has relied on auditors to identify patterns of behaviour that just didn’t quite fit. They often only detected problems months after the event. And then organisations had to claw back stolen funds through legal processes.
In a world where transactions happen in under a second, however, this is no longer acceptable. We need to be able to detect fraud immediately, if not before it happens. Customers want safe and protected data that is not vulnerable to identity theft through company systems. But they still want to be able to pay online and in seconds. The stakes are high, but fortunately new tools and techniques in fraud analytics are enabling companies to stay ahead of fraud.
Trusting machines to do the work
Machines are much better than humans at processing large data sets. They are able to examine large numbers of transactions and recognise thousands of fraud patterns instead of the few captured by creating rules. On the other hand, fraudsters have become adept at finding loopholes. Whatever rules you set, it is likely that they will be able to get ahead of them. But what if your system was able to think for itself, at least to a certain extent?
New approaches to fraud prevention combine rules-based systems with machine learning and artificial intelligence-based fraud detection systems. These hybrid systems are able to detect and recognise thousands of fraud patterns and learn from the data. Automated analytical-based fraud detection systems can reveal novel fraud patterns and identify organised crime more consistently, efficiently and quickly. This makes them a good investment for businesses across a wide range of sectors, including public sector, insurance, banking, and even healthcare or telecommunications.
How, though, can you harness analytics as a tool in your fight against fraud?
Identifying needs and solutions
The first step is to identify which options you need. Probably the best way to do this is through a series of company-wide workshops with the fraud analytics experts to determine what analytics you need, which data to include and techniques to use, and what results to report. They can also identify the ideal combination of rules-based and AI/ML approaches to detect fraud as early as possible.
Companies looking towards advanced analytics for fraud detection will need to make a number of decisions. They will need to optimise existing scenario threshold tuning, explore big data, develop and interpret machine learning models for fraud, discover relevant information in text data, and prioritise and auto-route alerts. There may be industry-specific decisions to make, too, such as automating damage analysis through image recognition in the insurance sector. By automating these areas, companies can both significantly reduce human effort – reducing costs – and improve their fraud detection and prevention.
Benefits of an analytical approach to fraud detection and prevention
Companies that are already using an analytical approach for fraud prevention have reported several important benefits. First, the quality of referrals for further investigation is better. Investigators also have a much clearer idea of why the referral has been made, which improves the efficiency of investigation. Analytics also improves investigation efficiency by reducing the number of both false positives (that is, alerts that turn out not to be fraud) and false negatives (failure to spot actual frauds). This improves customer experience and reduces risk to the company.
Analytics makes it possible to uncover complex or organised fraud that rules-based systems would miss. Companies can group together customers and accounts with similar behaviors, and then set risk-based thresholds appropriate for each scenario.
There are several sector-specific benefits too. For example, insurance firms can identify fraudulent claims faster to prevent improper payments from going out. Claims investigation is likely to be more consistent because claims are scored through technology, algorithms and analytics, rather than by people. Finally, it becomes possible to shorten the claims process through automated damage analysis. It is no wonder that organizations across a wide range of sectors are placing analytics at the heart of their anti-fraud strategy.
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