Article | February 23, 2020
Does the success of companies like Google depend on that of the algorithms or that of data? Today’s fascination with artificial intelligence (AI) reflects both our appetite for data and our excitement about the new opportunities in machine learning. Amalio Telenti, Chief Data Scientist and Head of Computational Biology at Vir Biotechnology Inc. argue that newcomers to the field of data science are blinded by the shiny object of magical algorithms and that they forget the critical infrastructures that are needed to create and to manage data in the first place.Data management and infrastructures are the little ugly duckling of data science but they are necessary for a successful program and therefore need to be built with purpose. This requires careful consideration of strategies for data capture, storage of raw and processed data and instruments for retrieval. Beyond the virtues of analysis, there are also the benefits of facilitated retrieval. While there are many solutions for visualization of corporate or industrial data, there is still a need for flexible retrieval tools in the form of search engines that query the diverse sources and forms of data and information that are generated at a given company or institution.
Article | September 7, 2021
Data has settled into regular business practices. Executives in every industry are looking for ways to optimize processes through the implementation of data. Doing business without analytics is just shooting yourself in the foot.
Yet, global business efforts to embrace data-transformation haven't had resounding success. There are many reasons for the challenging course, however, people and process management has been cited as the common thread.
A combination of people touting data as the “new oil” and everyone scrambling to obtain business intelligence has led to information being considered an end in itself. While the idea of becoming a data-driven organization is extremely beneficial, the execution is often lacking. In some areas of business, action over strategy can bring tremendous results.
However, in data governance such an approach often results in a hectic period of implementations, new processes, and uncoordinated decision-making. What I propose is to proceed with a good strategy and sound data governance principles in mind.
Auditing data for quality
Within a data governance framework, information turns into an asset. Proper data governance is essentially informational accounting. There are numerous rules, regulations, and guidelines to make governance ensure quality.
While boiling down the process into one concept would be reductionist, by far the most important topic in all information management and governance is data quality. Data quality can be loosely defined as the degree to which data is accurate, complete, timely, consistent, adherent to rules and requirements, and relevant.
Generally, knowledge workers (i.e. those who are heavily involved in data) have an intuitive grasp of when data quality is lacking. However, pinpointing the problem should be the goal. Only if the root cause, which is generally behavioral or process-based rather than technical, of the issue is discovered can the problem be resolved.
Lack of consistent data quality assurance leads to the same result with varying degrees of terribleness - decision making based on inaccurate information. For example, mismanaging company inventory is most often due to lack of data quality. Absence of data governance is all cost and no benefit. In the coming years, the threat of a lack of quality assurance will only increase as more businesses try to take advantage of data of any kind.
Luckily, data governance is becoming a more well-known phenomenon. According to a survey we conducted with Censuswide, nearly 50% of companies in the financial sector have put data quality assurement as part of their overall data strategy for the coming year.
Data governance prerequisites
Information management used to be thought of as an enterprise-level practice. While that still rings true in many cases today, overall data load within companies has significantly risen in the past few years. With the proliferation of data-as-a-service companies and overall improvement in information acquisition, medium-size enterprises can now derive beneficial results from implementing data governance if they are within a data-heavy field.
However, data governance programs will differ according to several factors. Each of these will influence the complexity of the strategy:
Business model - the type of organization, its hierarchy, industry, and daily activities.
Content - the volume, type (e.g. internal and external data, general information, documents, etc.) and location of content being governed.
Federation - the extent and intensity of governance.
Smaller businesses will barely have to think about the business model as they will usually have only one. Multinational corporations, on other hand, might have several branches and arms of action, necessitating different data governance strategies for each.
However, the hardest prerequisite for data governance is proving its efficacy beforehand. Since the process itself deals with abstract concepts (e.g. data as an asset, procedural efficiency), often only platitudes of “improved performance” and “reduced operating costs” will be available as arguments. Regardless of the distinct data governance strategy implemented, the effects become visible much later down the line. Even then, for people who have an aversion to data, the effects might be nearly invisible.
Therefore, while improved business performance and efficiency is a direct result of proper data governance, making the case for implementing such a strategy is easiest through risk reduction. Proper management of data results in easier compliance with laws and regulations, reduced data breach risk, and better decision making due to more streamlined access to information.
“Why even bother?”
Data governance is difficult, messy, and, sometimes, brutal. After all, most bad data is created out of human behavior, not technical error. That means telling people they’re doing something wrong (through habit or semi-intentional action). Proving someone wrong, at times repeatedly, is bound to ruffle some feathers.
Going to a social war for data might seem like overkill. However, proper data governance prevents numerous invisible costs and opens up avenues for growth. Without it, there’s an increased likelihood of:
Costs associated with data. Lack of consistent quality control can lead to the derivation of unrealistic conclusions. Noticing these has costs as retracing steps and fixing the root cause takes a considerable amount of time. Not noticing these can cause invisible financial sinks.
Costs associated with opportunity. All data can deliver insight. However, messy, inaccurate, or low-quality data has its potential significantly reduced. Some insights may simply be invisible if a business can’t keep up with quality.
As data governance is associated with an improvement in nearly all aspects of the organization, its importance cannot be overstated. However, getting everyone on board and keeping them there throughout the implementation will be painful. Delivering carefully crafted cost-benefit and risk analyses of such a project will be the initial step in nearly all cases.
Luckily, an end goal to all data governance programs is to disappear. As long as the required practices and behaviors remain, data quality can be maintained. Eventually, no one will even notice they’re doing something they may have considered “out of the ordinary” previously.
Article | August 9, 2021
The financial industry has been going through digital transformation for years. Digital technologies have helped to automate manual and tedious tasks like processing and reporting of historical data to forecasting and financial predictive analytics.
The financial services industry owes its success to data. Data is constantly evolving in the form of market trends, client investment, customer service, campaigns. Data gives a boost to banking strategies. As reported by Accenture in a recent survey, 78 percent of banks have made the shift to using data for operations; however, only seven percent of them have extended to using predictive analytics in finance.
Predictive analytics in finance has had a slow but steady start. It is an area of growing interest for banks and other institutions as new newer technologies launch in the market. To complete your company’s digital transformation, data analytics in finance will make a difference in that process.
To be successful, organizations must have the ability to adapt to changes.
Having predictive analytics on your side, your organization can deal with ever-changing circumstances with less to no difficulty.
Understanding Predictive Analytics: What is it?
Predictive analytics is a process of interpreting data to measure any possible future outcomes. It is carried out with the help of statistical modeling, historical data sets, and machine learning. The collected historical data is fed into an algorithm that recognizes patterns and forecast trends and possible future behavior from days to years in advance.
Analyzing historical data and predicting the future has been an old practice in the finance sector. Banks and financial institutions have been evaluating past events or historical data for a long time now.
Making precise forecasts in trends and analyzing data becomes easier due to predictive analytics.
There is a wider scope to predictive efforts with more speed and accuracy and apply them throughout strategic and tactical business practice areas.
Predictive Analytics in the Financial Sector: What are the Benefits?
Many organizations are ready to accept the positive applications of predictive analytics but remain skeptical about the return on investment.
It is worth understanding the potential of predictive analytics to any business big or small. It doesn’t matter if you are not in the banking sector to benefit from taking a peek into the future of financial performance.
Any finance and accounting department can take advantage of advanced predictive analytics for the following reasons:
The technology keeps a regular track of the consistency between expectations and reality to warn you about possible gaps.
Analytics accurately helps you identify any possible threats to your business and warns you.
Enhanced User Experience
Predictive analytics guides you to recognize the strengths of your business and lets you know how to maximize customer satisfaction.
Analyzed Decision Making
You can understand your customers better with predictive analytics. With this information, you can correctly match your customers with the product in a better way.
Importance of Predictive Analytics
Most successful banking and financial institutions depend on predictive analytics because it simplifies and integrates data to increase profits for companies. Predictive analytics can improve different finance processes.
But the importance of analytics goes beyond just banking services and actually goes into a better quality of customer service. Better customer service is only possible because of the advanced technology that shares customer feedback and preferences throughout the organization, in turn giving relevant information to every employee to make necessary product enhancements.
To understand the importance of predictive analytics, below are some of its use cases:
Predictive analytics in financial institutions and banking give you a complete profile of your customer base. It is impossible to contact every customer and interview them about their likes, needs and wants. This is where big data analytics in finance comes into play. It gives you the whole information about your customers regardless of the services they subscribe.
Customers usually don’t have the same needs throughout their lives. As they grow older and have families, their financial needs change accordingly. For instance, a young person considering getting married will always try and save monetarily to buy a house, life insurance, college funds, whereas an older couple will save that money for their retirement.
Apart from enabling different financial services, predictive analytics empowers you to serve individual customers with ease. Let’s take an example. When a customer applies for a loan, predictive financial services can help you analyze if the customer can repay the loan.
Predictive analytics also helps offer alternative services like secured loans to customers who may not qualify for the originally applied services.
Online Banking Made Better
Consumer interest fluctuates in spikes. Predictive analytics informs managers enough in advance so they can set up online infrastructures in those areas. Predictive analytics has made it easier to identify a possible customer base. For example, it can provide metrics to the marketing teams. In turn, the marketing teams can target the customers with ads for probable mortgage loans or business loans in hopes of converting them into their customers.
Data analytics in finance also helps in preventing and detecting fraud and abuse. Although detecting fraud doesn’t necessarily fall under predictive analytics, it can inform the IT department about potential scammers and which online services must be protected.
Foreseeing Market Variations
Predictive analytics can predict market variations and changes. By combining internal and external data, your organization can predict revenue growth in particular market sectors.
For nascent or growing companies, predicting market changes is an important ability. Profitable companies should also be reviewed through predictive analytics to generate demand projections owing to the uncertainties caused by the Covid-19 pandemic. Your return on investment can grow or reduce even with the minutest changes to the growth plans that would seriously impact investor confidence in the future.
Predictive analytics also help to establish which marketing campaigns are working and which strategies need to change.
Predictive Analytics and the Future: What Next?
Technological improvements have allowed predictive analytics in finance to improve and change constantly. Any organization can use customized data solutions to meet your customers’ needs and reach new ones efficiently. Your organization can use predictive analytics to move your business and products ahead and understand how the market will thrive, giving you the much needed heads up you would need to change your strategies and tactics.
Frequently Asked Questions
Is predictive analytics is the future of finance?
Predictive analytics is called the ‘future of financial software,’ which means it can provide accurate planning and cost-effectiveness.
How can analytics be used in finance?
Analytics helps in predicting revenue, improve supply chains, identify trouble spots, understand where the company is bleeding money, and fraud detection.
How do predictive analytics benefit financial institutions?
Predictive analytics can help financial institutions and customers detect fraud, financial management, predicting markets, improving products, better user experience, etc.
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Article | February 18, 2021
While digital transformation is proving to have many benefits for businesses, what is perhaps the most significant, is the vast amount of data there is available. And now, with an increasing number of businesses turning their focus to online, there is even more to be collected on competitors and markets than ever before.
Having all this information to hand may seem like any business owner’s dream, as they can now make insightful and informed commercial decisions based on what others are doing, what customers want and where markets are heading.
But according to Nate Burke, CEO of Diginius, a propriety software and solutions provider for ecommerce businesses, data should not be all a company relies upon when making important decisions.
Instead, there is a line to be drawn on where data is required and where human expertise and judgement can provide greater value.
Undeniably, the power of data is unmatched. With an abundance of data collection opportunities available online, and with an increasing number of businesses taking them, the potential and value of such information is richer than ever before.
And businesses are benefiting. Particularly where data concerns customer behaviour and market patterns. For instance, over the recent Christmas period, data was clearly suggesting a preference for ecommerce, with marketplaces such as Amazon leading the way due to greater convenience and price advantages.
Businesses that recognised and understood the trend could better prepare for the digital shopping season, placing greater emphasis on their online marketing tactics to encourage purchases and allocating resources to ensure product availability and on-time delivery.
While on the other hand, businesses who ignored, or simply did not utilise the information available to them, would have been left with overstocked shops and now, out of season items that would have to be heavily discounted or worse, disposed of.
Similarly, search and sales data can be used to understand changing consumer needs, and consequently, what items businesses should be ordering, manufacturing, marketing and selling for the best returns.
For instance, understandably, in 2020, DIY was at its peak, with increases in searches for “DIY facemasks”, “DIY decking” and “DIY garden ideas”. For those who had recognised the trend early on, they had the chance to shift their offerings and marketing in accordance, in turn really reaping the rewards.
So, paying attention to data certainly does pay off. And thanks to smarter and more sophisticated ways of collecting data online, such as cookies, and through AI and machine learning technologies, the value and use of such information is only likely to increase.
The future, therefore, looks bright. But even with all this potential at our fingertips, there are a number of issues businesses may face if their approach relies entirely on a data and insight-driven approach. Just like disregarding its power and potential can be damaging, so can using it as the sole basis upon which important decisions are based.
While the value of data for understanding the market and consumer patterns is undeniable, its value is only as rich as the quality of data being inputted. So, if businesses are collecting and analysing their data on their own activity, and then using this to draw meaningful insight, there should be strong focus on the data gathering phase, with attention given to what needs to be collected, why it should be collected, how it will be collected, and whether in fact this is an accurate representation of what it is you are trying to monitor or measure.
Human error can become an issue when this is done by individuals or teams who do not completely understand the numbers and patterns they are seeing. There is also an obstacle presented when there are various channels and platforms which are generating leads or sales for the business. In this case, any omission can skew results and provide an inaccurate picture. So, when used in decision making, there is the possibility of ineffective and unsuccessful changes.
But while data gathering becomes more and more autonomous, the possibility of human error is lessened. Although, this may add fuel to the next issue.
Drawing a line
The benefits of data and insights are clear, particularly as the tasks of collection and analysis become less of a burden for businesses and their people thanks to automation and AI advancements. But due to how effortless data collection and analysis is becoming, we can only expect more businesses to be doing it, meaning its ability to offer each individual company something unique is also being lessened.
So, businesses need to look elsewhere for their edge. And interestingly, this is where a line should be drawn and human judgement should be used in order to set them apart from the competition and differentiate from what everyone else is doing.
It makes perfect sense when you think about it. Your business is unique for a number of reasons, but mainly because of the brand, its values, reputation and perceptions of the services you are upheld by. And it’s usually these aspects that encourage consumers to choose your business rather than a competitor.
But often, these intangible aspects are much more difficult to measure and monitor through data collection and analysis, especially in the autonomous, number-driven format that many platforms utilise.
Here then, there is a great case for businesses to use their own judgements, expertise and experiences to determine what works well and what does not. For instance, you can begin to determine consumer perceptions towards a change in your product or services, which quantitative data may not be able to pick up until much later when sales figures begin to rise or fall. And while the data will eventually pick it up, it might not necessarily be able to help you decide on what an appropriate alternative solution may be, should the latter occur.
Human judgement, however, can listen to and understand qualitative feedback and consumer sentiments which can often provide much more meaningful insights for businesses to base their decisions on.
So, when it comes to competitor analysis, using insights generated from figure-based data sets and performance metrics is key to ensuring you are doing the same as the competition.
But if you are looking to get ahead, you may want to consider taking a human approach too.