Data is said to be the new currency.
Data monetisation is a hot topic as if every company can go about it easily, aggregating vast amount of data, use it to enhance current offerings and in the process disrupting established industries or transforming the way traditional businesses operate; or simply sell the Data to another company that finds more value.
However, very few companies are in the right business to start with, have the opportunity and ability to acquire the vast amount of relevant “Big Data”, or the resources and know-how to transform to a Data-Driven technology business.
That being the case, what business should and can focus on is to acquire the ability to use Data Analytics in every aspect of the business to be faster and better than the competitors in their decision making, increase bottom line and manage business cash flow more effectively.
Data Analytics is not new. “Business Intelligence” was first coined by Gartner as “the access to and analysis of quantitative information sources to deliver insight that empowers decision makers”. It is about Making Sense of Data in the context of enterprise decision making.
The value of Data Analytics is the extent of analytics that can help in decision making by increasing the confidence level and effectiveness of the decision.
In an organisation, there are three levels of Planning and hence decision making. Organisations plan and make decisions at the strategic level, setting out corporate goals and objectives.
At the strategic level, we are forward looking, analysing market trends, changing preferences of customers and carrying out product roadmap, growth in the new market and what-if scenario planning.
At the tactical level, we are looking at analysing data to understand how the business performs against the budget. Managers prepare departmental budgets rolled-up to corporate budget.
At the operational level, the planning decisions will be relating to the day-to-day supply chain, stocking up of inventories, mark-down pricing, buy-sell decisions. We are looking for insights – from descriptive to diagnostic data analytics; looking for explanations as to what happened, why did it happen and what can be improved or avoided.
At the operational level, enterprises want to improve decisions that are made by the day, by the hour, by the minute or even by the second – whether the retailer is trying to recommend products or a bank trying to persuade a customer to sign up for a cash-back or rewards based credit card. Data-driven decisions can be made on the basis of analysing customers’ interests, preferences and spending patterns.
Data science is the process of “asking the right questions, manipulating the data sets, and creating visualizations to communicate results” as defined by John Hopkins University in its course introduction to data science on Coursera (“the world’s best courses, online”).
In the Enterprise Data Analytics market, there are three broad platforms: Corporate Performance Management, Business Intelligence and Analytics as well as Predictive Analytics. They are largely differentiated by how, where and when they are being used corresponding to the levels of enterprise decision making; driven by product vendors as what they want to focus on and how they want to compete in the marketplace.
Corporate Performance Management (“CPM”) helps organisations define their corporate strategy in the form of Key Performance Indicators (KPIs) Balanced Scorecard objectives. A CPM platform allows for guided workflow-based top-down and bottom-up budgeting, planning and forecasting. This significantly shortens the time needed to complete entire cycle.
CPM is a platform that allows for:
• reporting of management, financial and operational performance against the budget;
• dynamic continuous rolling forecast for faster decision-making based on data, aligning it to corporate strategy and objectives in order for companies to keep pace with rapid organisational changes.
The key benefits of CPM platform are:
• allows the CFO to drive strategy to all parts of the organization with the CEO;
• motivates, manages, monitors and measures the execution of the organisation’s strategy; and
• allows users to drill down to the underlying data that leads to the achievements of KPIs or variances.
In short, CPM is about Management by Objectives. However, what is not clearly understood by many is that the underlying technologies of CPM platform leverages on data analytic technologies.
In CPM Platform, in-memory multi-dimensional online processing (“OLAP”) analytics technology stores data in a multi-dimensional cube. Each data point in the cube has a specific set of coordinates from the dimensions by which it is addressed. OLAP technology allows data to be analysed from various dimensions. The main advantage of CPM Platform is that it allows users to not only extract, load and transform data for visualisation but also allow users to budget and plan data to be written back or stored into the cube for business performance reporting against the budget.
Leading enterprise software vendors, SAP, Oracle, IBM and Microsoft have had Business Intelligence software for many years. However, there was little innovation until Data Visualisation based analytics platform built on high-performance in-memory engine with the ability to do ad-hoc analysis of millions of rows of data in seconds. Qlik and Tableau, both Data Visualisation based analytics platform took off in 2004, growing rapidly, competing neck to neck behind Microsoft PowerBI and now recognised by Gartner as the leading Business Intelligence and Analytics (“BIA”) platform today.
Data Visualisation is about displaying patterns and trends beyond simple bar, pie and line chart to include, mekko, block chart, radar and heatmap, geographic maps, scatter plots to enable users to make sense of data. Data Visualisation is primarily Management by Exceptions and helps users identify patterns, outliers providing visual analysis of data for purpose of decision making.
Machine learning is the embedding of algorithms in systems that collect data from past decisions, iteratively learning why those decisions were made. In short, it is building systems to be able to learn to produce repeatable and reliable decision making in the future.
Predictive Analytics is about the use of data mining to perform customer profiling and segmentation, machine learning through algorithms and statistics to model why past decisions were made, turn uncertainty as to customers buying behaviour in the future into usable probability, infer outcomes and anticipate future events or the behaviour of customers in their decision making.
While Business Intelligence and Analytics are about Management by Exceptions; Data Science and Machine Learning are about learning from exceptions. Leading global software vendors in this category include Tibco Spotfire, RapidMiner and SAS. Due to innovations, potential applications and more vendors offering predictive and machine learning capabilities, Gartner has started to term this category of software in its latest 2018 Magic Quadrant research report as Data Science and Machine Learning Platform.
Traditional enterprise software CPM vendors have been slow but there are pockets of innovation. Jedox AG from Germany has been able to stand out. Jedox AG has conducted innovation and poured investments into building a world class product, Jedox Unified Corporate Performance Management and Business Intelligence Platform. The seamless Software Platform is now paying off with more than 2300 customers globally.
Jedox is designed for the Office of Finance for budgeting and planning, allocating resource and driving activities, reporting of performance against plan. Jedox is a collaborative platform for enterprise planning, data analytics, and reporting.
Jedox offers rich visualizations and dashboards which are built upon outside-in Design Thinking using easy to use Jedox web spreadsheets which Finance users would find familiar, collaborate, share and capture data, and explore dashboards and reports – anytime, anywhere.
Jedox easily integrates data from multiple source systems such as ERP, CRM, HRM and other operational systems; connectivity to any data source in the cloud or on premises. Jedox Integrator provides over 50 out-of-the-box connectors as well as full extraction, transformation, and load (ETL) capabilities. Visualize all your integration projects automatically to orchestrate master data management and support data audits. Once data are integrated and loaded into Jedox In-Memory Database, users would be able to perform self-service analytics on their everyday MS Excel using Jedox Excel Add-in without the need to involve IT department
Jedox is designed to meet enterprise decision making challenges at all levels of the organisations, using MS Excel to connect to Jedox multidimensional database with data from all your data sources. Combining sophisticated data governance, workflows, and audit capabilities, Jedox delivers self-service planning and create insightful analytics and business intelligence dashboards with a fresh, intuitive user experience, giving every user the choice to work from the web, on the mobile, or in a familiar, native MS Excel environment.
In decision making, Artificial Intelligence and cognitive offerings tend to remove humans from decision while “Augmented Intelligence” is about combining the best of what machines can do with human input and interaction. Jedox AIssisted™ Planning promotes better machine learning and in doing so, better supports businesses in the area of Revenue and Cost Forecasting, Recommendation/ Personalization, Dynamic Pricing and Predictive Maintenance. In this way, the platform delivers the most Business Value to organisations.
Jedox AIssisted™ Planning for Enterprises includes AI for Data Validation & Cleansing, Augmented Forecasting, Driver-based Planning and Enhanced User Experience. With Augmented Intelligence, Jedox is innovating from Unified Business Intelligence and CPM Platform into an Enterprise Data Analytics platform.
Business fundamental has always been about generating an increase in cashflow month on month, quarter after quarter, year after year, to meet operating expenses while still turning a profit and to further invest in the growth of the business to meet shareholders’ expectation of dividend growth or capital appreciation.
Data Analytics matters to CFO as it leads to a continuous assurance of enterprise data when all levels of management have enterprise performance visibility and insights into its Order to Cash and Procure to Pay processes, how Investments are optimised and Funds are utilised.
The benefits are lesser surprises, better allocation of resources when the business is more responsive and aligned to market changes and changing consumer preferences; constantly improving cash flow position.
The CFO is well positioned to partner the CEO to drive the digital transformation of the organisation. However, the CFO also has to cope with Governance, IFRS convergence and the perennial transfer pricing tax compliance challenges. Success as CFO these days requires more than just delivering on Governance and Financial Reporting.
The imperative that matters now is to digitalise the finance function with the upskilling of the Office of Finance in Data Analytics being crucial to the success of driving the adoption of Data Analytics for enterprise decision making at all levels of the organisation.
What is the CFO to do? How is the CFO able to discern what are the suitable Enterprise Data Analytics Platform to use and how to deploy it to gain deeper insights of their business operations to respond better to ever increasing complexity to gain competitive edge in the business environment?
CFOs can gain faster line of sight into their business operations and to be next to the CEO in the use of Data Analytics to drive digital transformation; increase profitability, optimal management of cash and business performance through data-driven decision making.
Our team can assist you from Strategy to Insights by understanding and mapping out with the Office of Finance, plan and roll out the implementation of the right-fit, Value for Money (“VFM”) Enterprise Data Analytics platform.