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.
Collaborative Data Preparation and Data Analytics will combine business users’ need for agility and data access for analytical purposes with IT’s desire for with governed, secure processes to manage data usage. Enterprise, team-based Data Preparation and Analytics will give companies the agile, intelligent infrastructure for true data-driven decision-making. Firms are already implementing integral parts of this new approach to Data Preparation and Analytics. Enterprises create an intelligent Data Management platform that will ensure positive business results and operational processes.
There are plenty of great ideas and techniques in the data space: from analytics to machine learning to data-driven decision making to improving data quality. One might expect to see companies trumpeting sustained revenue growth, permanent reductions in cost structures, dramatic improvements in customer satisfaction, and other benefits. Except for very few, this hasn’t happened. It takes a lot to succeed with data. As the figure below depicts, a company must perform solid work on five components, each reasonably aligned with the other four. Missing any of these elements compromises the total effort.
Big data and machine learning are part of a single architecture, a powerful duo that together can protect against even the most complex threats. A strong cyber security platform requires an inbuilt data management platform that collects and organizes big data, in combination with machine learning algorithms that analyze this data, respond to threats, and prevent against new attacks. Without big data analytics and machine learning, it would be impossible for security professionals to gather and organize the heaps of security events and to interpret all potential threats.
Every enterprise is talking about Business Intelligence and Advanced Analytics. But, before your organization selects and deploys a solution, there are numerous important considerations. Choosing and implementing a solution for Advanced Analytics and Augmented Data Discovery is not as simple as buying team t-shirts for your company baseball team. If you do not take the time and effort to do it right, your enterprise may spend a lot of money and time on a solution that reaps little to no benefit.
It’s no mystery that data opens many new doors for both the disruptive data unicorns of the 21st century and traditional companies seeking to optimize business benefits. Due to the value of sensor data, the Internet of Things (IOT) and Industrial Internet of Things (IIOT) offer great promise to organizations seeking to establish entirely new markets or enable greater, more beneficial differentiation. Only time will tell if established market leaders can adapt to the modern data environment using purpose-built, cloud-optimized data analytics platforms, equipped with machine learning, or be rendered irrelevant by the more agile data-driven upstarts.