Ranking: Top 10 Trends To Watch in Financial Analytics

Accenture defined CFO as Architect of Business Value in 2014. Going back to then we may remember that many companies had deep silos of inconsistently defined and structured data, making it difficult to extract information from different parts of the business. But what's new for Financial Analytics?

  1. Digital is killing finance organization → Tweet this! ′In God we trust, all others must bring data′ said engineer and statistician W. Edwards Deming. Growing opportunities to store and analyze large amounts of data from disparate sources is driving many leaders to change their decision-making process. So now the challenge is how to enable each responsible in your company to access that big data, in the right way.
  2. Data gravity will pull analytics to the cloud → Tweet this! Cloud data warehousing is no longer an option so the question for Information Technology now is "When?". Then, you better have analytics there.
  3. Compliance audit is still a challenge → Tweet this! Big data coming from new unstructured data sources are also increasing new risk but also new opportunities. Self-service data preparation is becoming key to improve a better data audit. Gartner ensures that the next big market disruption is self service data preparation and that by 2019 it will represent 9.7% of the global revenue opportunity of the business intelligence market.
  4. Organizations should focus analytics on decisions, not reporting → Tweet this! Gartner introduced the concept of algorithmic business in 2015 to describe the next stage of digital business and because by 2018, over half of large organizations globally will compete using advanced analytics and proprietary algorithms, causing the disruption of entire industries.
  5. Analytics is not only about reporting anymore → Tweet this! Analytics are maturing and priorities are naturally quite focused on the business side. Algorithms will not only provide insights and support decision-making, but will make the decision for you.
  6. 49% of the companies are using predictive analytics and 37% are planning to use it within 3 years → Tweet this! TransUnion reports profit for 2015 after booking losses since 2012, when moving from relational databases to predictive and embedded analytics. Before the overhaul, IT staff spent half its time maintaining older equipment. About 60% of capital spending went to caring for legacy systems. Now it′s 40% to 45%, they said. 77% of companies cite predictive capabilities as one of top reasons to deploy self-service business intelligence.
  7. Financial data is the leading indicator when using analytics to predict serious events → Tweet this! Recent cases like MasterCard's one proves that there is growing interest in using analytics to get ahead of serious social and political developments that affect corporate as well as national interests and financial data is a leading indicator.
  8. To know your customer and to deliver relevant data is a key business differentiator → Tweet this! First things first - no matter if you are forecasting risk of fraud, crime, or finance outcomes.
  9. What will make emotional analytics really helpful is to have a stronger analytics model behind it → Tweet this! Risk or fraud can be identified by sentiment analysis on social media, so you are able to prescribe future actions. But if you look at sentiment or emotion alone, it essentially flags a lot of false positives.
  10. The need to manage, monitor, and troubleshoot applications in real-time has never been more critical → Tweet this! Digital transformation grows as the reliance on new software, so the need for full-stack visibility and agility is the true business demand underpinning the growth of analytics.

The Financial Analytics Landscape

Data Integration involves bringing together data from different sources and providing users with a unified view of these data.

Wikipedia

Data Mining is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems.

Wikipedia

Predictive Analytics encompasses a variety of statistical techniques that analyze current and historical facts to make predictions about future or otherwise unknown events.

Wikipedia

Data Visualization involves the creation and study of the visual representation of information that has been abstracted in some schematic form, including attributes or variables for the units of data.

Wikipedia

Embedded Analytics is the technology designed to make data analysis and business intelligence more accessible by all kind of application or user.

Wikipedia

Business Intelligence is the process for the acquisition and transformation of data into meaningful and useful insights for business analysis purposes.

Wikipedia

OpenText within Analytics Landscape

Market Landscape for Financial Analytics

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How To Identify Patterns Using Association Rules

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