Augmented Analytics Delivers the Insight You Need
December 12, 2017
Global commodity markets are more complex and volatile than ever. Commodity market participants can no longer afford to wait days or weeks to understand market changes, analyze alternatives, and make decisions. The key to making the most profitable decisions lies in the data businesses are generating, but current systems cannot analyze the volume, variety, and velocity of data generated in a timeframe that is useful.
Making sense of all this data
Analytics have become easier to use, but most companies with exposure to commodities still use manual processes for preparing data, analyzing it, and interpreting results. Others rely on generic business intelligence solutions not designed for commodity analytics. Neither will provide the real-time insight you need to stay ahead of competitors for several reasons.
- Necessary data is contained in disparate systems, including external sources – companies must aggregate and analyze data from multiple sources – ERP, CRM, C/ETRM systems, spreadsheets – in addition to external data from market feeds, sensors, weather forecasts, and more.
- Analytics remains the domain of IT and other specialists – real-time data loses its value when it takes days or weeks to analyze it, a reality when IT departments or data specialists are the only ones equipped to run analyses.
- Generic BI solutions lack domain specific algorithms – generating useful insights requires commodity-specific analytics to answer commodity market participants’ most critical questions, not generic business questions.
- Visualization is not enough – visualization provides businesses a partial view into their data, with no actionable or proactive insights. For truly useful analytics, businesses need to connect and analyze rapidly growing amounts of data and create predictive and prescriptive insight.
What commodity businesses need to navigate all this data is augmented analytics. Augmented analytics uses machine learning and natural language processing to automate data preparation and analysis. This advanced preparation, analysis, and presentation of data provides deep insight with clear results so business users can make better decisions faster. Augmented analytics quickly analyzes millions of variable combinations to generate unbiased answers to users’ critical business questions – in significantly less time than it would take to analyze the data manually.
The augmented analytics you need
Eka Analytics provides the augmented analytics you need to make better, fact-based decisions. Eka’s always-on Intelligence Engine uses industry, function, and role specific algorithms to analyze data from disparate data sources – C/ETRM, CRM, spreadsheets, IoT sensor data, and ERP, and as well as external sources like market feeds, weather reports and more – to answer your most important questions with one clear answer.
Eka Analytics’ machine learning algorithms wade through the volume, velocity, and variety of data produced each day.
- Traders can explore data coming in from multiple sources to discover new insights and increase profits.
- Risk managers can investigate hidden risks faster and more effectively.
- Analysts can leverage multiple, complex algorithms and predictive models at a fraction of the time and effort.
- Supply chain and logistics managers can use advanced visualization and statistical optimization to make the best scheduling decisions and improve collaboration.
- IT specialists can quickly and easily respond to user requests for custom analytics.
- Back office personnel can analyze metrics to improve financial operations including confirmations, invoices, cash, and settlement.
- Executives can monitor all key performance metrics across the business and identify potential issues before they become problems.
Eka Analytics provides fact-based, predictive, and prescriptive decision support to enable businesses to make better decisions in today’s complex and volatile business environments.