Welcome to Week 3 in our blog series ‘7 emerging trends that are changing finance’. This we take a look at Greater Visibility. As new technology improves data collection and accuracy, advanced analytics technologies are making big data more accessible, giving finance professionals greater visibility into their organisations.
Previous Posts on 7 emerging trends that are changing finance:
3. Greater Visibility
CFOs get greater visibility into their business
As finance professionals adjust into more strategic business leadership roles, the importance of having objective data to analyse is increasingly important. Big data has delivered big promises, but one of the most major challenges for big data has been managing the volume and speed. A 2015 study by IBM18 reported that 2.5 quintillion bytes of data are created every day, so much that 90% of all data in the world has been created in the last 2 years. Without the tools to better leverage their data, its user have remained relatively niche, but with more intelligent and powerful cloud computing, big data is finally moving into new areas, helping finance leaders close books faster, deliver more accurate reporting, and build more intelligent business strategies.
Big data becomes accessible
The merging of big data with new technology has made processing large datasets easier than ever. From mining big data to predictive analytics, finance leaders have tools available to them that did not exist previously. These professionals have been asked to apply their systemic approach for numbers to data that reaches beyond financial data. This often includes assessing consumer data to forecast purchase trends, economic indicators to predict market trends, and operations metrics to help streamline processes and cut costs. Beyond dollars and cents, CFOs possess the ability to extract knowledge from numbers and apply that knowledge to make strategic decisions about the business.
Beyond data analysis, CFOs face another new, modern day data challenge. As they’ve taken on larger roles within IT and analytics, CFOs are forced to tackle the growing issue of data management. This includes both data storage, as well as monitoring and managing data quality. These important tasks not only enable CFOs to do their job, but they allow other functions to operate more efficiently. Without data quality control, CFOs and other business leaders risk making decisions based on flawed insights.
While faster, more reliable data is a stride in the right direction for big data, it also creates new challenges, what IBM has coined, “The Four V’s of Big Data.” The four V’s include volume (scale of data), velocity (analysis of streaming data), variety (different forms of data), and veracity (uncertainty of data).
With the growth in big data, finance professionals are definitely feeling these challenges. In a 2014 1010data study, 41% of finance professionalscited inconsistency in measurement methodology as their biggest obstacle to analytics success, while 38% cited a lack of granularity, and 34% saying that they had insufficient access to the data they needed. In short, big data is too big. To date, companies’ ability to leverage their data has been limited due to a lack of internal knowledge, limited tools, and prohibitive costs, but corporate capabilities are finally catching up.
Insights into operations
New technology, from smaller sensors to more ubiquitous Internet access, now provides finance professionals access to faster, more reliable data. Remote data sensors are becoming increasingly common, and they’re just getting started. This trend is fueled by four factors: 1) the ability to produce smaller microchips, 2) the development of low-power sensing technologies, 3) the ubiquity of Internet connectivity, and 4) improved computer processors that are able to manage a growing number of concurrent data streams. Perhaps the fifth variable would be the increasing comfort level consumers have with data collection.
These advancements allow finance organizations to collect data from a much wider range of sources than was previously possible, including data from retail accounts, online transactions, manufacturing operations, customer profiles, economic data, and other KPIs. With more accurate, comprehensive, and real-time data, finance professionals can gain a greater understanding of who their customers are, how their business is running, and how different growth strategies may affect their bottom line.
Tools to help anticipate business needs
Finance professionals are now relying on a new breed of analytics tools that make big data more easily accessible, including machine learning, predictive analytics, and automation tools. Big data, in and of itself, is not useful; it becomes useful when it can provide knowledge, make processes more efficient, and allow finance professionals to deliver better experiences. These tools help finance professionals with detection, classification, probability, and optimization.
Detection involves identifying patterns (trends), targets, and outliers. Data visualization tools that convert large sets of numbers into charts and graphics makes trends much easier to identify, and allow for simple, visual comparisons that can make outliers jump out. The addition of real-time data adds speed to the simplicity that data visualization provides. Finance professionals may use these tools to track competitive changes, news, campaigns, or promotions; detect emerging market trends; or to identify potential supply chain issues before they materialize.
Classification is used quite often in business, although we may not consciously define it as such. Classification allows us to isolate, sort, filter, sequence, and compare data. Classification helps finance professionals become more agile by enabling them to isolate and/or rank audience segments, campaigns, or products. Tools that provide the ability to automate these processes can streamline personalization and allow finance professionals to be more targeted with their outreach. This also empowers finance professionals to create personalized experiences and conduct more intelligent cross-selling. In Q1 of 2015, Microsoft Australia dropped bounce rate on their website by 35% and increased add-to-cart rate by 10% by creating fully personalized product recommendations into a fully personalized web experience for customers.
Understanding the probability of future events is critical to making strategic decisions. Probabilities can be used to demonstrate the likelihood of an occurrence, to compare multiple events, or to show distributions of potential outcomes. Predictive analytics tools help finance professionals master inventory management, determine optimal pricing for new products, offer more targeted product recommendations, predict sales and support needs, and better manage cash flow.
As agility is becoming increasingly important for finance professionals, optimization is an important use of analytics that helps finance professionals become more nimble. This can be done by optimizing the three pillars above: better and faster detection, categorization, and probability. As more real data becomes available, new machine learning tools help finance professionals improve performance by comparing the expected results against their actual results and optimizing their algorithms accordingly.
While many of these methods are not new, new tools, like more powerful processors paired with cloud computing, are now enabling finance professionals to take full advantage of their data.