The key to achieving BI success by making it accessible to everyone starts with generating insights, then operationalising those insights and being able to place a monetary value on the benefits gained. The goal is to turn data into actionable insights with real business outcomes. However, there are several common mistakes organisations make when rolling out BI and analytics projects that result in their investments ending up as shelfware: unused, forgotten and representing missed opportunities.
Tax fraud is already prevalent, and fraudsters are more sophisticated and automated than ever. To get ahead of the game in detecting fraud and protecting revenue, tax agencies need to leverage more advanced and predictive analytics. Legacy processes, systems, and attitudes need not stand in the way. What’s new in fraud prevention and what does a complete capability look like? What can Tax agencies do differently and better today than they could a few years ago? This blog explores the challenges, opportunities, and value of tax fraud analytics
Advanced analytics is the logical tool to help a business optimize its investments and achieve its goals. But, when an organization is ready to consider the implementation of an Advanced Analytics solution, it is difficult to know what it needs to ensure that it can satisfy current and future requirements and ensure user adoption. If a business wants to assure that it has full coverage for its Advanced Analytics needs and can leverage all the benefits of advanced analytics, it should consider a solution with the necessary capabilities.
It is very difficult for organisations to discern which tools will bring them the most benefit, and which issues they need to plan for. New technological developments provide the platform for the next generation of innovation, as we’ve seen with the evolution of ‘Big Data’ into advanced analytics, machine learning, and artificial intelligence. How can businesses navigate this increasingly-complex data landscape to make the wisest investments? Here is our guide to the top seven data trends that should be on every organisation’s radar for the year ahead.
Big data Is just one tool for managing risk. As we mentioned above, there are three pillars to effective risk management in modern times: oversight for regulatory compliance, a strong company culture focused on making the right hires and training on the right principles, and the wise application of useful technologies. One of the most important advantages of bringing data analytics into the mix versus relying on the other two pillars alone is that the company's analytics platform gets smarter with each new data point it receives.
How to measure something that by its nature is abstract and unmeasurable, like team collaboration? What KPIs would you use to assess the overall state of team collaboration and ensure its long-term monitoring to draw unbiased conclusions? Overall, there are a plethora of software solutions created to evaluate personal performance and monitor employees’ development. However, those solutions can hardly deal with collaboration assessment, or they require substantial customization effort to handle such a non-trivial task. Happily, big software providers have started to incorporate relevant functionality into their core systems to prevent organizations from investing in stand-alone solutions.
By unifying analytics, building forecasts and accelerating analytic processes, simulation helps companies build a holistic picture of their business to optimize strategy and maximize revenue. Here are the four types of information that companies need to fuel simulation forecasting and monetize their data investments. Once a company identifies sources for these four types of data, it’s time to find an effective way to monetize it. With simulation forecasting, reliable answers are accessible – and you may need less data than you think to get meaningful, trustworthy insight.
Many organizations have grown comfortable with their business intelligence solution, and find it difficult to justify the need for advanced analytics. The advantages of advanced analytics are numerous and those advantages are based on the ability to further improve the business, increase user adoption and therefore user empowerment and accountability and, best of all, improve the bottom line and the accuracy of predictions and forecasts that will dictate the success of the business in the future.
Traditional cloud computing architectures need to evolve to a more decentralized approach that processes data at or near the source. Not only could edge computing provide this capability, but it has the potential to increase data computing efficiencies. While traditional data centers will remain the core computing power for enterprises, we’ll begin to see edge computing technology become integral into data center strategies in 2019. Whether this means integrating a solution into current operations or small data centers built for edge analytics, the next year will be the year enterprises live on the “edge.”
Organizations across industries are adopting graph analytics to reinforce their anti-fraud programs. Most anti-fraud applications are able to connect simple data points together to detect suspicious behavior. But these applications fall short on more complex analysis. The graph databases we’ve seen emerge in the recent years are designed for this purpose. In this post, we examine three types of fraud graph analytics that can help investigators combat insurance fraud, credit card fraud, and VAT fraud.
Today, predictive analytics is, and must be, accessible to business users, if your enterprise is to grow and respond to the need for data democratization and increased productivity within the enterprise and to the rapid changes in the market, competition, resource and supplier needs and customer buying behavior. Every business user must have the tools to analyze data and make accurate, timely predictions and decisions. Your organization can truly benefit from predictive analytics and from the ease-of-use and sophistication of these self-serve tools.
Data Science advances statistics from its mathematical roots to more balanced math, data, and computational foci. The challenges were foremost of data management and computation – assembling, wrangling, cleaning, reporting, and managing the data. The statistical work was far downstream from implementation of the then-new relational database system to manage the data. The ascendance of open source changed the analytics landscape fifteen years ago, with databases like PostgreSQL and MySQL, agile languages such as Python and Ruby, and the R statistical computing platform, encouraging an even greater commitment to analytics and facilitating the emergence of companies whose products were data and analytics.
I use the suitcase dilemma as a metaphor for the types of decisions being made in the analytics technology world by customers. Companies invariably confront with the decision on the type/size/complexity of solutions to implement, and they many times initially demanded the 100% answer to their forecast needs into the mid to long range future. Be a suitcase-skeptic – don’t be too quick to purchase the largest, handle-all-cases bags. Consider in addition the frugality and simplicity of a 95+% solution, simultaneously planning for, but not implementing, the 100% case.
One of the most important elements of advanced data discovery and Advanced Analytics tools is plug n’ play predictive analysis and forecasting tools. These tools can support the enterprise initiative to implement Self-Serve Advanced Analytics and transform business users into Citizen Data Scientists. Plug n’ Play Predictive Analytics provides easy-to-use tools that require no programming or Data Scientist skills and enable the average business user to leverage sophisticated predictive algorithms so users can confidently plan for success.
Companies articulate openly their need to shift their organizations culture to become more data-inspired in decision-making related to strategic and tactical decisions. And analytics have become an important part of the decision-making process for many companies in the past few decades, particularly with corporations using data assets as a core competency and point of origin. The dynamic nature and improved capabilities for analytics enable companies and even individuals to do more and in better ways. Here are six predicted trends to watch for in the coming new year.
Data can help surface the apparently invisible, but stronger undercurrents of human behavior. Data analytics can be a potent tool to better understand employees and their engagement levels in organizations. It can transform the length and breadth of the HR function, from reducing hiring bias, improving employee relationships, finding drivers of performance, to helping manage attrition. Here are 3 steps for success in the journey towards smarter talent management. To illustrate each of these points.
Augmented Analytics automates data insight by utilizing Machine Learning and Natural Language Processing to automate data preparation and enable data sharing. This advanced use, manipulation and presentation of data simplifies data to present clear results and provides access to sophisticated tools so business users can make day-to-day decisions with confidence. Users can go beyond opinion and bias to get real insight and act on data quickly and accurately. Why is this important to your organization?
As data analytics becomes an increasingly critical component of pharma innovation, the problem of data silos stands out as a barrier urgently in need of attention. Here we discuss how data silos in pharma prevent the latest advancements in machine learning and data analytics from operating at their full potential, how AI and blockchain are transforming incentives in data sharing practices to promote greater transparency, and how companies can leverage open data to drive innovation and lower drug prices.
Advanced Analytics is comprised of numerous sophisticated analytical techniques, designed to parse, explore and analyze data and produce results to support business decisions. Fortunately, today’s new self-serve business intelligence solutions allow for ease-of-use, bringing together these varied techniques in a simple interface with tools that allow business users to utilize advanced analytics without the skill or knowledge of a Data Scientist, analyst or IT team member. Advanced Analytics provides a 360-degree view of data from Data Marts, Data Warehouses, best-of-breed, legacy systems, and other data sources.
A proper Information Management system – including healthy Enterprise Content Management (ECM) and modern archiving strategies – will definitely help to manage and integrate existing data and content across the enterprise, as well as limit or overcome the pollution of such information, by granting chain of custody and compliant governance along the data’s entire lifecycle. Modern ECM and archiving systems provide all the compliance, enterprise-grade functionalities to solve these requirements and enables organisations to start their journey to the new world of big data with the right approach and clean data streams to fill their data lakes with.
The global healthcare Data Analytics market will grow, as healthcare companies increasingly use data for financial applications, and to improve operational and administrative processes. The industry’s reliance on Data Analytics is also being driven by the increased use of electronic health records (EHRs) as well as the digitization of financial records and insurance claims processing. While the use cases for data in healthcare are endless, in this post, we’ll take a look at how analytics outcomes can specifically impact administrative and financial offices in healthcare organizations of all types and sizes.
Despite having more data, it’s difficult to extract value from it in a timely fashion. If you want to be fast and agile with your data, you need a strategy built on enabling you to do that. Your data strategy needs to include more than just raw processing power. This is where data warehousing comes in – offering unified, governed, large-scale support for analytics. When it comes to your data warehouse, you need a way to get it moving quickly – and automation can help.
To drive innovation to a whole new level we’re seeing a rise in the use of Natural Language Processing (NLP) and Artificial Intelligence (AI) to make finding and using trusted, quality data that much easier. In fact, leading industry analyst firms have noted that Machine Learning, AI and NLP are quickly becoming table stakes for analytics. That requires significant heavy lifting at the infrastructure level and it’s not an easy thing to do.
The world of Business Analytics has changed dramatically in the past few years. If your business is looking to upgrade BI tools or to begin implementing an Analytics Solution, the solution must be user friendly for business users. The tools exist today for Augmented Analytics, Augmented Data Discovery, Self-Serve Data Preparation and other features and modules that provide sophisticated functionality and algorithms in an easy-to-use dashboard and environment that is designed to support business users, as well as Data Scientists and IT staff.