Analytics is the key to unlocking future potential.
CFOs need to work with CIOs to convert analytics into an enterprise-wide capability. The need to improve analytics capabilities and data management is the top driver of collaboration between CFOs and CIOs based on findings from an EY survey of 652 global CFOs. But only 53% of CFOs say that they make a significant contribution to determining where analytics can add most value to the organization.
“Companies don’t have analytics challenges or opportunities. They have business challenges and opportunities, which analytics can play a role in tackling.”
— Chris Mazzei, Global Chief Analytics Officer, EY
Five success factors for CFO-CIO collaboration on analytics:
- Take a business-led approach, not a technology-led one
- Make data a fourth pillar of the business, and leverage it as an asset
- Do not forget the human element of analytics
- Build the right organizational structure and governance framework
- Consider legal, regulatory and trust issues throughout the journey
Fraud detection and prevention
Fraud detection and prevention
For business executives in multiple functions and across many industries and geographies, “big data” presents tremendous opportunities. For those charged with preventing, detecting and investigating misconduct, mining such data can be a particularly powerful tool to be utilized in compliance and antifraud management efforts.
Companies are increasingly seeking growth in markets with higher perceived levels of fraud, bribery and corruption risk, while regulators and law enforcement bodies are intensifying their cross-border collaboration in investigations. At the same time, the costs associated with noncompliance with global anti-corruption statutes or a company’s own antifraud policies and procedures are growing.
Out-of-date risk assessments, undetected frauds and poorly executed investigations, followed by failure to properly remediate internal controls, only exacerbate the risks facing companies.
- Our forensic data analytics (FDA) combines the extensive use of big data and statistical and qualitative analysis, in conjunction with explanatory and predictive models, to guide and identify issues and areas warranting further review.
- Our fact-based evidence drives actionable business decisions, focuses investigative efforts where it matters and optimizes outcomes.
- Our suite of FDA service offerings comprises proactive and reactive methodologies that leverage the information contained in large-scale, structured and unstructured client data sets. This allows us to more efficiently detect and investigate instances of error, waste, misuse, abuse, corruption, noncompliance and fraud, or to assist in a regulatory or litigation response. With the integration of third-party data into our suite of FDA tools, our professionals are able to effectively exploit internal data with relevant external or third-party intelligence, such as country sanctions lists, legal proceedings or adverse media checks.
- Our FDA platform avoids unnecessary client costs by transforming large disparate data sets into actionable analyses and patterns that assist in answering the critical questions at issue.
The internal audit function must embrace analytics to keep pace with or outpace the business; it must become a natural part of the thought process. This will involve not only the adoption of new tools and techniques but also a change in mindset. Internal audit’s use of data analytics can help the business improve its processes and deliver even better products and services. Properly developed, analytics can help internal audit provide business insights and act as a strategic advisor while holding the line on costs or even reducing them.
Where is your internal audit function on its analytics journey?
Each organization and function will need to tailor the analytics strategy and delivery model to its internal audit vision, mandate and plan, making key decisions on the strategy, methodology, talent, knowledge and tools along the way. The questions below can help you frame a discussion on your journey and determine the best way forward:
- How can you be sure you’re fulfilling your internal audit mandate in an efficient and effective manner?
- To what extent do you rely on information and analytics from the business to identify risk?
- What role does innovation and continuous improvement play in your internal audit culture?
- How do you manage the analytical skill set in your internal audit function, including career paths?
- How are you aligning your function’s capabilities and talent to keep pace with the explosion of data in your organization?
- Do you have a defined governance model and strategy in your organization?
- How does your internal audit methodology incorporate analytics?
- To what extent are you managing data throughout the audit life cycle?
- What are the objectives and related benefits of analytics in your internal audit function?
- What methods have you developed to measure the business benefit delivered from your use of analytics?
- How are the results of analytics embedded in your audit reports? Do the reports include data-driven findings and visualizations to corroborate the findings?
While the big data revolution has created substantial benefits to businesses and consumers alike, there are corresponding risks that go along with it.
The need to secure sensitive data, to protect private information and to manage data quality exists whether data sets are big or small. However, the specific properties of big data (volume, variety, velocity, veracity) create new types of risks that necessitate a comprehensive strategy to enable a company to utilize big data while avoiding the pitfalls.
Seven key steps gauge whether you are ready to start benefiting from big data:
Good governance encompasses consistent guidance, procedures and clear management decision-making. Organizations need to ensure standard and exhaustive data capture; they need not protect all the data, but they need to start sharing data with built-in protections with the right levels and functions of the organization.
- Given the ubiquitous nature of big data, does your data governance framework acknowledge the evolving definitions of data owners and consumers?
- Does your current governance address the risks related to the life cycle of big data?
Integrating and moving data across the organization is traditionally constrained by data storage platforms, such as relational databases or batch files with limited ability to process very large volumes of data, data with complex structure or without structure at all, or data generated or received at very high speeds.
- Do you have the right skills and internal capabilities to deal with the big data technologies and methods, which are relatively new?
- Do you have sufficient control over the big data volumes, variety, velocity and veracity, which may impose additional risks?
Data architecture should be prepared to break down internal silos, enabling the sharing of key data sets across the organization and to ensure that learnings are being captured and relayed to the right set of people in the organization in a timely and accurate manner.
- Does your IT infrastructure support your big data strategy?
- Can you flexibly scale processing and storage to meet the demands of big data processing?
The results of big data can be beneficial to a wide range of stakeholders across the organization: executive management and boards; business operations and ri sk professionals, including legal, internal audit, finance and compliance; as well as customer-facing departments, such as sales and marketing. The challenge is having the ability to interpret the huge amount of data that can be collated from various sources.
- Do you have the right talent to be able to process, model and interpret big data results?
- Is your workforce ready to shift to the new paradigm of data-driven decisions?
The quality of data sets and the inference drawn from such data sets are increasingly becoming more critical. Organizations need to build quality monitoring functions and parameters for big data. Correcting a data error can be much more costly than getting the data right the first time — and getting the data wrong can be catastrophic and much more costly to the organization if not corrected.
- Are your existing methods sufficient to deal with the unstructured data?
- What level of data quality is required to meet your big data goal?
Companies need to start establishing security policies that are self-configurable: these policies must leverage existing trust relationships, and promote data and resource sharing within the organizations, while ensuring that data analytics are optimized and not limited because of such policies.
- Is your security infrastructure robust enough to deal with the increasing demands of protecting a growing stockpile of data, while flexible enough to not become bottlenecked by the innovation?
The increased use of big data challenges the traditional frameworks for protecting the privacy of personal information, forcing companies to audit the implementation of their privacy policies to ensure that privacy is being properly maintained.
- Have you defined who owns big data information, and whether there is actual or implied consent to use the same?
- Do you understand that how the big data is stored and how it is used can also create significant privacy issues?
Technology has empowered consumers t o be smarter, better informed and more demanding than ever. They are adept at filtering information, move effortlessly between channels, expect value and quality in return for their money as well as personalized offers to be matched up with their needs.
The digital purchasing journey starts with the proliferation of smartphones and tablet devices and has given consumers instant access to powerful technology whether they are in the workplace, at home or on the move.
Whatever the sector, businesses face stiff competition. Advanced analytics and delivery of an outstanding customer experience hold the keys to success.
Customer experience: think customer, act analytics
In this era of big data, the ability to deliver true insight is far more than useful — it’s vital.
As smartphone-wielding consumers spend ever more time using digital channels, analytics comes into its own. In order to really understand today’s digital consumers’ complex behavior, advanced analytics must be embedded across the company.
This means new systems, new processes and new ways of working. Even though this may be expensive, the positive impact will be huge and the investment worthwhile.
Dealing with data is the key to tax transparency and to effective management of companies’ growing indirect tax obligations.
Tax reporting begins and ends with data – but the variety of indirect tax data required by different jurisdictions and the sheer quantity of relevant data now generated by large organizations can present logistical issues for its effective collection, storage and analysis.
With the increase in reliance on value-added tax (VAT), customs, excise and other indirect taxes by governments to meet budgetary needs and the "fair tax" debate putting companies’ tax affairs firmly in the spotlight, many multinational organizations are required to report more – both in frequency and quantity.
Tax and customs administrations are requesting more information about companies' transactions and where and with whom they do business.
Through the use of data analytics, these tax administrators are analyzing and comparing data from different organizations to collect and protect tax revenues. Data extraction is also helping them to perform "smarter" tax audits to identify underpayments and systemic weaknesses, as well as carry out risk-based audits. In-depth reviews that once took from three months to two years to complete can now be done on a data-driven basis in a matter of weeks.
In the past few years, "big data" has become a key focus in companies and tax administrations on a glob al scale. Enterprise intelligence is how companies manage and exploit their big data. Until recently, too few companies have been able to meet the challenge in their own tax data management and response times. Taxpayers are still very reactive, and this data is analyzed and consolidated predominantly as and when the tax administration performs a tax audit.
As tax and customs administrations around the world develop their data analytic capabilities, taxpayers must do the same or they will find themselves at a distinct disadvantage and at risk.
Supply chain & operations
How can analytics help transform the supply chain?
Complex supply chains require sophisticated, connected tools to drive optimal performance, enable better operational decision making, monitor risks, predict disruptions, and support rapid recovery as part of an overall supply chain strategy. This requires companies to increase their adoption of advanced tools grounded in analytics and visualization to help improve operational efficiency across their end to end value chain and meet customer demand.
At EY we help clients to turn insights into action by leveraging these advanced analytics tools to transform every aspect of their supply chain to improve forecasts, demand planning, procurement, production and distribution and improve manufacturing operational performance and supply chain risk.
We help clients to unlock the full value from their strategic assets by bring a unique set of deep analytical, operational, change management and financial skills, with other EY professionals from commercial, finance, tax, customs, risk and sustainability, supported by alliances that deliver the leading operational excellence and know-how. We have a number of strategic digital and technology alliances such as GE Digital, Cisco, IBM, Microsoft, PTC and SAP, with whom we co-innovate to bring the latest disruptive technologies in a practical way, to tackle complex operational problems. We also have unique industry alliances to bring the best of the industry to specific supply chain challenges. For example, EY collaborates with Procter & Gamble on manufacturing excellence based on their proprietary analytical tools and methods that can help achieve sustainable breakthrough results. To learn more, read: Unlocking manufacturing’s full performance potential.