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The Digital Finance Function

Digital and technology advances are profoundly transforming the finance function from a number cruncher to an enterprise data and analytics powerhouse. Disruption is taking place at alarming levels and for CFOs, it is no longer a case of whether they should respond to this change or not but rather how and how quickly. They need to be able to make sense of this digital economy, drive economic value and improve business decision making.

Internet of Things (IoT), Big Data and Analytics, Machine Learning, Cloud, Robotic Process Automation (RPA), Security Threat Intelligence, In-memory Computing, Mobile and Artificial Intelligence (AI) are all enabling businesses to:

  • Transform their supply chains.
  • Anticipate the future, mitigate risks and take advantage of opportunities.
  • Deliver efficiencies, accelerate business growth and improve profitability.
  • Redefine their operating model, become more agile and responsive to changing market conditions and customer expectations.
  • Get closer to existing and potential customers, understand their needs and wants and create unique experiences and solutions.

In order to realize the above benefits and digital’s full value it is imperative to change the organization’s finance operating model and adopt new ways of working.

Investing in digital is a strategic move rather than a technological issue. Many digital investments fail to take off from the ground because management view these an expense and not an enabler of strategic success. There is therefore an urgent need to change this perception. For instance, embracing digital technologies can help finance apply advanced analytics tool sets to volumes of structured and unstructured data, make sense of this data, and produce real-time reporting and business insights.

Changing the perception that investing in digital is an expense requires finance to develop an activist mindset. CFOs must build a strong business case for embracing digitization, help business leaders understand what the digital advances mean for their business units and determine the appropriate strategies and capabilities needed to respond.

Important to note though is that not every organization has a use for every digital technology in the market. You need to identify and select a tool based on the specific needs of your business. Thus, CFOs must develop a coherent digital finance strategy that is aligned with the business strategy. Technology alone is not the answer to your business needs. In order to experience real digital transformation, the business must also have the right support systems in place, from the optimal talent mix to the appropriate operating model.

Furthermore, for finance to successfully embrace digital technologies and making a positive impact to the business, the function must quickly adapt its skills set around digital and IT innovations. Artificial Intelligence and RPA are taking over many routine and rules based accounting and finance roles. This means finance professionals must move beyond their traditional historical reporting role to a more predictive analytical and business partnering role.

They need to sharpen their analytical capabilities, ask the right questions from structured and unstructured data sets, turn the analysis into commercial insights and drive business strategy across economic, market, competitor and customer perspectives. Today’s finance professional has to be more collaborative and strategically focused, engaging with the business and delivering insightful advice.

As data and analytics continue to transform businesses, it is no longer advisable for the finance organization to fill up with accountants only. In order to exploit to full potential the internal and external data the business holds, finance must be made up of diverse teams with different skills sets (Data Science, Analytics, Statistics, Behavioural Economics, Systems Thinking etc.) to encourage creativity and debate.

With digital opportunities also comes threats. The number of cyber incidents is growing exponentially thereby increasing the risk to the business. Phishing emails, Trojans and other multiple virus attacks are some of the security challenges that CFOs have to deal with on a continuous basis. Because the finance function is normally the custodian of sensitive information within the business, it is imperative that the CFO is on top cyber security. You need to have answers to the questions below:

  1. Do you know where your information is at all times?
  2. How the information is stored and kept safe?
  3. Who might want to steal it (disgruntled employees, criminals or hacktivists.)?
  4. How can intruders gain access to the information?
  5. What is the financial impact of a cyber-attack?
  6. In the event of a cyber-attack, do you have a clear and credible contingency plan?

It is therefore critical that finance takes the lead in assessing and advising the Board on all cyber-security matters. You need to identify the most valuable assets that differentiate your business and are in most need of protection.

There is no doubt that digital and IT advancements are reshaping the way we live and work. Organizations that are quick to embrace these changes and make innovation an every day part of their business will more than likely reap benefits in the long-term.

Because of its analytical capabilities, finance is best positioned within the business to drive the digital transformation agenda and act as a reliable source of analytic insights since the function is able to connect structured and unstructured data from various sources and produce reliable insights from its analysis.

I welcome your thoughts and comments.

Transforming Finance Into An Analytics Powerhouse

As organizations continue to change at an unprecedented pace, the role of finance also continues to expand and transform. The function must do more than just reporting results and provide forward-looking analysis that supports strategic decision-making processes and enhances business performance. This increased pressure on CFOs to be more business partners and strategic partners is renewing the call for finance to embrace and be at the forefront of data analytics to guide smarter decision making.

CFOs Must Cultivate a Data-Driven Culture

Businesses are operating in an economy that is more technologically driven and data-centric. Digitization, increased globalization, changing business models, increased volatility and a changing regulatory environment continue to pose challenges on businesses, especially with regards to decision-making.Unfortunately, data alone is not enough to make smarter decisions.

Making smarter decisions requires organizations to develop capabilities that enable them to quickly and easily transform this raw data into useful insights. These insights must be available to management in real-time otherwise they will end up working with a lot of “Dead Data.”

Finance is already used to dealing with large amounts of data and because the function is centrally positioned within the business to oversee various key decisions, CFOs should work more closely with business teams in driving their analytics agenda. For example, they can:

  • Ask business leaders critical questions they expect data analytics to answer. The more CFOs and their analytical teams continue to probe, the better the insights generated. In a constantly volatile environment, management must be able to model various what-if-scenarios and their outcomes.
  • Provide data-driven insights in the areas of pricing, inventory management, supply chain optimization, customer profitability and M&A, thereby demonstrating the value brought to the business by analytics.
  • Deploy dashboards that not only show financial metrics, but also operational, customer and process metrics and allow business leaders to drill down to the specifics themselves and make improved decisions.

Expand CFO Influence Outside the Finance Function

Traditional financial data from legacy ERP systems is no longer the main driver of decisions. Today’s businesses have more data (Structured and Unstructured) than in the past, and the rate at which this data is being produced continues to increase at alarming levels. The predicted growth in data is exponential, with some experts predicting a 4,300 percent increase in annual data production by 2020.

It is not a case of collecting data and leaving it to become obsolete and irrelevant before it can be used for the purpose it was collected for. Decision makers are depending more on insights derived from data to make better decisions. In the fight against cyber crime, companies are using predictive analytics to identify anomalies within their systems, assess vulnerabilities, predict attacks and automatically resolve. No longer are companies relying solely on threat signatures to fight cyber crime.

In the retailing industry, companies are using analytics to understand customer preferences, segment customers, create market differentiation and improve margins. In other organizations, analytics are being applied to improve and strengthen operations. IoT devices are helping companies assess, monitor and enhance machine performance.

This above examples alone show how data has become a strategic asset. By owning and driving analytics initiatives within their businesses, CFOs can continue to expand their strategic leadership role, strengthen their ties throughout the organization, expand their influence outside the finance function and become strategic business partners.

Adopt Modern Analytical Systems

Advancement in technologies and the growth in Shared Services business models has reduced the amount of time finance executives spend on transactional and routine activities. Today, much of the CFO’s time is spend on strategic issues, for example, helping the CEO and other business leaders execute strategy, identifying M&A opportunities, purchasing and implementing IT systems, creating shareholder value, assessing and monitoring risks,  and driving business performance.

In order to continue delivering on the above, CFOs must reduce their reliance on disconnected analytical data processes and legacy analytical systems, and invest in analytical capabilities that enable them to execute strategy more effectively, reduce processing cycle times, improve financial productivity and reduce finance operating costs.

Spreadsheets have their role in analytics but it is important to note that upon reaching a certain level, they become limited. As the business grows and the amount of data produced increases, it is worth investing in a data analytics system that is suited to your business needs and helps you achieve your strategic objectives. This is not just about replacing spreadsheets and the old software with the new system and tools. Instead, it is about understanding the fact that the new system is just an enabler and not your lottery ticket to riches.

By becoming an analytics powerhouse, the finance function  will be able to model various what-if-scenarios and provide the foresight to predict future outcomes, the insight to make real-time strategic decisions, and the hindsight to analyze and improve historical performance. Overall, the organization will have an advantage over its competitors.

I welcome your thoughts and comments

 

Data Analytics and the FP&A Function

September 5, 2016 Leave a comment

Technological advancements in Big Data and Analytics are having a significant impact on the business’s operating model and strategic performance. Many companies are already exploring how best they can adopt big data and analytics technologies to improve their businesses,  reduce costs, streamline processes,  improve marketing initiatives, and pursue future profits. In the majority of these organizations,  the marketing function and supply chain are leading the pace in applying these new analytic capabilities and serving the customers. Unfortunately, finance is lagging behind and still holding on to its legacy systems and primitive technologies.

All is not yet lost, there is still hope for finance to embrace advanced analytical technologies and help drive business performance. In addition to helping marketing and supply chain functions, Big Data and Analytics can too play a critical role in supporting the finance function fulfill its FP&A role in today’s dynamic business environment. In a world awash with large volumes of data, unstructured and structured, being able to identify patterns, anomalies and derive strategic insights is key for effective decision making. Having this ability to access, synthesize and monetize data requires the FP&A function to invest in new skills and data tools and take advantage of the potential uses of new data types.

It is therefore imperative for the CFO to consider the implications of investing in Big Data and Analytics technologies as well as the impact of using data for effective decision making. Modern technologies are not the domain for the CIO only but also for the CFO. Finance must learn to partner with the business, understand the language of IT and develop an ability to identify and evaluate the various ways data analytics technology can help the FP&A function. CFOs should be asking themselves, how best can they leverage Big Data and Analytics technology to help improve the organization’s budgeting, planning and forecasting processes? How best can they enrich operational and financial forecasts with the most reliable data and make them more accurate?

While everyone is talking about Big Data and Analytics these days and how they have the potential to transform the organization and create a competitive advantage, it is easier for management and executives to join the “Big Data Dream” without first formulating a clear and coherent data strategy. In the end, these executives end up collecting large volumes of data, most of it being worthless, resulting in the business incurring significant data costs and suffering from ineffective decision making.

The value in data is found when the organization is able to collect, synthesize, analyze and retrieve strategic insights from that piece of data and improve decision-making process. Key to consider prior making significant investment in data analytics technologies is the alignment of data strategy with the broader strategy of the organization, data access and governance issues, new skills requirements, and implementation road map.

One of the challenges facing many FP&A functions is assessing the relevant data to analyze, identifying trends and gaining valuable strategic insights. When preparing forecasts and analyzing business performance, there is need, for example, to synthesize data across operational, financial and customer information. How can all this data be integrated and used for decision support purposes? Unfortunately,  not many finance professionals possess this ability to manage this new data and new data types in a way that creates visibility across the organization and benefit other functions. Thus, it is crucial for the FP&A function in today’s economy to develop data mining and analysis capabilities to ensure relevant data is being used for strategic and performance improvement decision making processes.

There is a need therefore for the organization to radically change its data approach and evaluate how it fits well within the overall strategy of the business. Ensure effective KPIs, measures and metrics are designed and implemented to help managers run the business. This approach will ensure that information requirements as well as investments in advanced technologies are not managed in silos but rather, in a deliberate and organized fashion that aligns and supports the broader strategy of the business. Furthermore, as data becomes a strategic asset to the organization,  it is crucial that the CFO collaborates with other senior executives of the organization and engage them in planning conversations and collaboratively find ways of improving business performance.

In today’s economic environment, data is found everywhere, both internally and externally and this data can only be accessed if finance decides to leave its comfort zone and begin engaging with the business. Unfortunately, many finance professionals have a strong technical background and are weak when it comes to soft skills. Walking around the business, initiating conversations with other functions and asking smart questions doesn’t come naturally to many finance professionals. However, as the role of finance continue to evolve, the finance organization must learn to adapt and acquire new soft skills to avoid being left behind in the back office.

Today’s finance professional is required not only to have data skills, but also the ability  to communicate with the broader organization, have a strategic mindset and a deeper understanding of the operational areas of the organization as well as the ability to identify opportunities and help the business grow by  reducing costs, evaluating and increasing top-line revenues. FP&A must be able to ask smart questions and identify the relevant business needs that can be addressed by data analytics. How can the business benefit from technology, make smarter and faster decisions and also become more efficient? How can an investment in data and analytics technology help the business identify emerging or unknown risks areas and manage these risks intelligently?

By taking advantage of data analytics technologies, the FP&A function will be able to identify business performance leading indicators based on the data available, adjust forecasts and drive that information into operations. Uncertainty and volatility in the global economy  are on the rise and because of this, many business executives are worried and cautious about where to make large capital expenditures. As the support function with a larger visibility across the organization,  the FP&A function can help address this uncertainty.

By embracing modern technologies, the function can transition from business intelligence and reporting on what happened, to data mining and understanding why something happened, to predictive analytics and determining what is likely to happen and when. This will in-turn help  management and executives put contingency plans in place and become proactive.

Taking a phased approach is necessary when implementing data analytics technologies.  Instead of going full force with the implementation,  the organization can start small and use a specific business unit as the basis for the pilot project. Results from this experimentation can then be used to evaluate technology performance in terms of ease of use, speed and benefits. If the results are satisfactory,  the next business unit or region is selected and the process continues until there is a complete roll-out across the entire organization.

 

Unlocking Opportunities in Big Data

February 21, 2014 Leave a comment

The Chartered Institute of Management Accountants (CIMA) in joint collaboration with the American Institute of Certified Public Accountants (AICPA) released a report on how organizations can unlock the value of big data and drive business performance. In addition to comprehensive desk research, over 2000 CFOs and finance professionals working in a broad range of sectors across more than 80 countries were surveyed for the report.

Data has become a key focus for business leaders today and is changing the way business is done. The volume, variety and velocity of data available for analysis is expanding exponentially and this wider shift towards a data-driven business model can be attributed to recent advancements in IT. Widespread adoption of enterprise resource planning (ERP) systems, electronic point of sale (EPOS), ecommerce and other internet-based systems has allowed more and more data organizational data to be captured digitally.

There is now an unlimited array of data sources for the organization. Examples of these data sources include mobile devices, call centre recordings, external data feeds, machine-generated data, customers’ social media posts etc. Data generated from all these sources includes a mixture of structured internal data and typically unstructured external data that may be used to strengthen analysis and forecasting and yield new insights into business performance, risks and opportunities.

This data explosion requires organizations to focus on developing new skills, invest in new tools and adapt new ways of thinking. The more data becomes a core business asset, the more the organization must learn to adapt to this data-driven business era and determine how it will use data to improve business performance. This means having the ability to find and extract insights from its data.

In this era of data-driven decision-making, finance professionals are well placed to transform data into commercial insights and value through planning, budgeting, forecasting and performance management. In other words, finance must know how to aggregate outcomes so that they can be converted into insightful reports.

To truly unlock the opportunities in big data, finance and accounting professionals need to partner closely with IT, data scientists and business leaders who in turn will turn their insights into action. Furthermore, finance professionals must have the ability to clearly communicate, lead and influence as well as possess a strategic understanding of the business.

The report also identifies five traits of the data-enabled CFO and these include the following:

  • Able to identify which data points are more useful in understanding what drives the business.
  • Have a clear sense of what customers care about most, why they choose the organisations’ products and services, how those customers are acquired and what helps retain them.
  • Able to embrace new forms of data and creative ways to incorporate this into business decision-making.
  • Comfortable with uncertainty, including the reality that big data may not provide definite answers.
  • Explore new ways to interpret data to better inform management decision-making.

Instead of relying on accounting data that is typically historical in nature, finance is now increasingly required to provide a real time forward-looking perspective of corporate finance. Investing in new analytics techniques and tools which are capable of managing and analyzing large amounts of data can help management draw new insights from data.

Organizations that choose to ignore the value of data in decision-making run the risk of losing out to others who are improving performance and gaining new insights from their data. Opportunities arising from big data include:

  • Ability to identify new opportunities for cutting costs or increasing efficiency.
  • Improved development and monitoring of KPIs that are aligned to corporate strategy.
  • Ability to prepare driver-based forecasts i.e. basing financial forecasts on operational drivers.
  • Ability to identify, assess and monitor external risks.
  • Increased revenues through better customer segmentation etc.
  • Ability to improve the responsiveness of decision-making and strategic planning.

Although new analytics tools play an important role in analyzing data and extracting insights from it, before delving into the world of unstructured data analysis, organizations should first identify the key business questions they actually want answered. In other words, the organization needs to fully understand its business model and its intangible assets, its data structures, data quality and data sources.

Once the problem to be solved is defined, the organization must then identify the data needed to answer its questions. In order to successfully inform decision-making or performance management, the data to be used must be reliable and accurate. Finance need to take the lead role of extracting insights from the data available within the organization. At the beginning of a data analytics project, Finance must ask the right questions and also ensure that any insights generated are actually used to aid decision-making and drive business performance. The ability to manipulate data and present it in many ways that are insightful and relevant to the audience is important for driving performance.

The following five steps are essential for creating a data-centric business:

  • Understand what new data would be relevant to your business model and competitive position.
  • Assess what data initiatives are already in place within your business.
  • Identify potential quick wins or small-scale proof of concept projects.
  • Conduct a formal data project to develop a related strategy.
  • Build on this initiative to start developing a data culture and ensure that data is regarded as an asset of the business as a whole.

In conclusion, finance needs to look beyond historical reporting and recognize the commercial potential of embracing a wider set of data. Furthermore, finance will need to review other non-traditional sources of data in order to gain more thorough understanding of business performance.

 

The Strategic CFO: Taking Finance Beyond Reporting

February 20, 2014 Leave a comment

Once regarded as stewards of organizational assets, financial executives in today’s information-driven market must become more strategic. Focusing only on financial accounting, cost reduction and compliance requirements is no longer considered enough to improve business performance and drive growth.

As technology continues to evolve, financial executives and their teams must adapt and respond to change, take advantage of the opportunities presented by new technology and move beyond reporting to analysis. This means elevating their business planning and analysis capabilities. In the past decade, finance has mostly focused on internal controls and cost-cutting initiatives. However, as organizations push into new and different businesses this is presenting an opportunity for financial executives to be more strategic and play a crucial business partnering role.

Instead of spending more time on transactional activities which add little or no value at all, financial executives and their teams must spend more time on value-adding activities solving problems and helping business leaders perform better. For example, business leaders need reliable and timely information to make strategic decisions  that relate to diversifying product and service lines; developing new business models; increasing or improving R&D, increasing production capacity; mergers and acquisitions or divesting certain parts of the business. Finance can therefore play an important role of delivering higher quality information and better analytics to executive management as well as provide more support to business unit line managers.

To successfully execute their business partnering role, financial executives must ensure that the financial and performance management information they are presenting to senior management is of high quality and detailed. In other words, decision-making information must be available quickly, wherever and whenever business managers and their employees need it. Furthermore, the presented information must also be actionable.

It is therefore important to ensure that the financial close is done faster. Unfortunately, in some organizations the financial close takes between 15 and 20 days. By the time information is delivered to management, you are almost at the next month before you are telling the business how you performed last month and in most cases this information is no longer relevant and useful for strategic decision-making. The faster the financial close, the quicker the finance team can deliver actionable information to management, the quicker management understand business performance and make relevant and fact-based decisions.

Today change is happening very quickly and in turn the organization must be able to adapt and respond. By using information strategically and as an important asset, executive managers are able to make confident calls on strategic issues such as entering new businesses or exiting current businesses that are not strategically fit. Because of the information already within their domain, financial executives are uniquely positioned to be the strategic information leaders. For example, they can embrace developments in business intelligence and analytic tools. Implemented and used correctly, these tools assist managers gain access to insightful information which in turn helps them improve decision-making, drive growth and best increase return on invested capital.

Financial executives that need to become trusted advisors and create value for their organizations must start partnering with the business instead of staying behind the scenes. They must move closer to where the action is happening and empower business strategy. In addition, they should become strategic players by implementing enterprise performance management methodologies such as scorecards, dashboards, strategy maps, customer profitability analysis, predictive analytics, rolling forecasts, dynamic pricing, enterprise risk management and value-based management.

The Analyst’s Role in the Business Analysis Model

As organizations continue to invest in business intelligence and analytics in order to improve and drive business performance, it is imperative that they do not become too technically oriented to the extent that investment in these tools fails to deliver the required information and knowledge necessary for creating and preserving value.

Used properly, business intelligence and analytic tools can help managers collect, store, analyze and interpret vast amounts of data which in turn helps them gain positive insights capable of improving tactical, operational and strategic decision making. On the contrary, if the company does not have an information strategy that is clearly aligned to corporate objectives the chances of achieving these objectives and improving business performance are limited.

In order to take advantage of the proliferation of big data and its merits, many organizations are investing heavily in the creation of data warehouses. The idea is to give the organization a central information platform which ensures consistent, integrated and valid data across source systems and business areas. Raw data on itself rarely delivers value to the organization. It has to be transformed into information and then knowledge for it to make sense for decision support.

Unfortunately, many investments in information systems continuously fail to deliver the desired results. This is due to the fact that management do not fully understand and comprehend the overall strategy of the business and as such investments in information systems are misaligned. In other words there is a huge gap between the organization’s information strategy and its corporate strategy.

When there is this disjoint, information technology and systems managers tend to become more technically oriented and lack the business acumen to make IT investments an enabler of business strategy execution. The danger of being too technically oriented is that operating and maintaining the company’s information systems’ structure often end up being an objective itself.

Because the management team lacks understanding of the value-adding element of a data warehouse, such investments end up becoming a huge cost. Even data of poor quality and useless to the organization will be collected and stored. It is therefore important that the company does have and information strategy that clearly uses the data warehouse as a means of attaining business objectives.

To ensure that your organization does not become a too much technically oriented, you need to first and foremost make sure that processes are in place that promote coordination between the business and the data warehouse. Analysts play an important role in disseminating invaluable information and knowledge from the data warehouse to the business decision makers. It is therefore critical that organizations invest sufficiently in the people side of the information system if they are to reap positive results.

Remember that a chain is only as strong as its weakest link. Even if the company invests huge sums of money in its data warehouse but lacks analytical competences able to contribute the required analysis and business insights for executing strategies and improving business performance, such a situation means that the investment made by the organization is in fact merely a cost, as nothing valuable comes out of it.

In today’s information age where possession of information and knowledge is a key differentiator, organizations must enhance their analytical capabilities. Those employed in the role of business analysis should move beyond managing data and delivering reports, tables or lists within a few days to being able to retrieve and process data. To successfully do this, analysts must understand the business processes they are supporting and how the delivered information or the delivered knowledge can make a value-adding difference at a strategic level.

In summary, closing the gap between technically oriented and business oriented environments requires management to treat information as a strategic asset. In such an environment, analysts understand and are able to convey to the business the potential of using information and knowledge as competitive parameters. Furthermore, the analysts are able to continuously engage themselves in dialogue with the business, detect and create synergies across functions. Instead of isolating themselves to the domains of IT, they see themselves in the bigger context of the business.

Implementing EPM Software

While there has been significant interest and adoption in performance measurement and management software by organizations over the past decade, technology still remains one of the most misunderstood and badly managed areas of performance measurement.

Many organizations have failed and some are still failing to achieve the intended results from their measurement-related technology implementations. This is so because the majority of business leaders view technology as a system itself. What they fail to understand is the role of technology in transforming performance measurement and management.

Technology plays an important role of supporting the performance measurement system. As long as the organization’s performance measurement system is bad, business leaders should never anticipate technology to make it good. Business leaders who are always in search of a quick fix (believing that investment in technology will solve all their measurement problems) without fully understanding the focus, context, interactivity and integration of performance measurement are bound to have their fingers burnt sooner or later.

As organizations evolve from having too little data to having too much of it, the challenge on them is to make sense out of this data in order to improve decision-making. Unfortunately, many business leaders are not interested in taking the necessary time required to transform measurement data into insights, insights into knowledge, and knowledge into wisdom that aids strategic decision-making and drive business performance.

Because they don’t want to be involved, automation becomes the answer. Instead of making decisions based on real knowledge and social aspects, software is allowed to take over and do the thinking. These business leaders join the bandwagon of software implementations (scorecards, dashboards, business intelligence and analytics) that provide little or no insights.

Although technology plays a critical role when it comes to collecting, storing and analyzing vast amounts of data, it falls short when it comes to interpreting the data for the purposes of sound decision-making. This is where the human element of performance measurement comes in. Adopting and implementing technology will not solve problems that lie within the organization’s measurement system.

Automating a broken process often results in an automated broken process. What is important is for business leaders to understand that delegating decision making to analysis tools at the cost of good human judgement is potentially damaging. It is also important to note that every performance measurement-related technology has limitations.

Performance measurement and management technology is effective only if it is capable of turning measurement data into insights, knowledge and wisdom that can be used for evidence-based decision-making. The purpose of technology and the organizational culture both play a critical role if the implementation is going to be successful. Business leaders must create a platform that promotes positive adoption. If people view the measurement technology as a performance monitoring tool aimed at punishing them if they miss targets, then it is most likely that the adoption will be poor.

What then is the proper role of technology? Measurement technology helps people positively deal with business difficulties and the explosion of big data through data mining which produces insights that aid decision-making. Furthermore, technology also helps people perform various tasks they cannot do effectively themselves or do inefficiently. For example, technology can measure more, more quickly; can automate data collection; reduce data handling errors; perform “what-if” analysis; facilitates simulation and predictive modelling as well as present data in almost any form.

Data from measurement systems must be used to create dialogue on performance measurement. Overdependence on technology may lead to poor identification of the critical few performance measures, poor differentiation from the many unimportant measures and poor performance management. For example, if any organization has data on almost every transaction with its customers, this does not mean that relationships will improve.

Fostering a culture focused on real customer relationship improvement is more social game than technical. Technology solutions will not provide the desired results unless the social and organizational enablers are in place.

Big Data: Lessons from the Leaders

The business landscape is being shaped by data as never before. Data is growing in volume, variety and velocity. The sheer magnitude of data being produced is staggering. Everyone is talking about big data but do executives and their subordinates really understand what it is exactly or how they can transform their organizations and drive business performance. Sadly enough, most companies do not know what to do with the data they have, much less the new forms of data.

In 2012, the Economist Intelligence Unit surveyed 752 global executives across a broad range of sectors to learn how high-performing companies are leveraging big data and through SAS sponsorship released a report, Big Data: Lessons from the Leaders. The report details great insights and lessons on how these leaders have learned and managed to positively exploit data, secure the right talent, leverage social media and web-tracking analytics, and demonstrate the strong link between financial performance and well-defined data management strategy. I recommend reading the full report. Highlights of the research include:

  • There is a strong link between financial performance and effective use of big data. The survey findings suggest that companies should fully exploit the data they are collecting. High-performing companies are effectively using big data to improve company performance across many areas, with strategic decision-making and operational efficiency highlighted most frequently. For example, retailers are using data generated by loyalty cards to better understand customers and insurers are now routinely analyzing large data sets in order to predict claims, root out fraud and set prices.
  • Companies become successful at exploiting data by focusing on business priorities. In order to transform their businesses and become successful, executives and their subordinates must place big data at the heart of the business. According to the survey, 59% of executives from leading high-performing companies have placed a high rating on data usage in all the business areas. To exploit big data, businesses should start by prioritizing the challenges or problems they want to solve and then build an appropriate data management strategy around those objectives. In other words, businesses should start by agreeing on a focus and with that focus in place, prioritize, manage and process data accordingly. Companies lacking a focused data strategy are most likely to under-perform and suffer than their more successful rivals.
  • Talent matters as much as technology. It is tempting to think that technology alone can transform a business. One of the findings from the global survey is that companies are still facing the challenge of securing analytic talent that possess knowledge of the sector the company operates and the right skills required to work with large data sets. To deal with the problem of analytic skills shortages, executives must ensure that analytic thinking is not confined to the IT department. Managers across all parts of the organization should be thinking about how data can improve performance and, with the help of data experts, transform those thoughts into actions. Some organizations are connecting data professionals in academia with their businesses in order to tap in their knowledge and expertise.
  • Social media analytics and web-tracking technologies can transform the way business collect data about customers. Data can do many things for the company. For example, a well-defined data-driven approach can be used to improve the efficiency of the supply chains, guide marketing efforts and spur innovative thinking about the customer experience. The evolution of social media networks has led to an explosion in “unstructured” data such as comments and opinions posted by customers that can provide insights into how a company is viewed. Through the use of web-tracking technologies and the analysis of user-generated content on social networks, companies are better placed to devise more effective methods for enticing customers away from rivals and achieve dramatic improvements in revenues and bottom line.

Closing Thoughts: Evidence of the advance of data is everywhere. A company should know that exploitation of data is critical to drive business performance and achieve a leading competitive edge over its rivals. If the company fails to exploit data, a rival will gain competitive edge by doing so. A well-thought-out data strategy is essential, as is a focus on acquiring employees who can combine sophisticated data skills with knowledge of the competitive landscape.

Source: Economist Intelligence Unit Report – Big Data: Lessons from the Leaders

Developing Executive and Operational Dashboards

Almost every successful executive, manager and supervisor uses dashboards to track strategy, operations and tactics. These leaders are using dashboards to keep their strategies and operations on track and detect hot spots or areas of concern within the organization. The use of dashboards by management has increased considerably over the past years because managers have realized that these tools give them the big picture about the business, puts them in real-time touch with the business and ultimately help them gain better insights and make better decisions.

Recent developments in Business Intelligence and Real-time Analytic tools that offer not only descriptive and predictive analysis help but also prescriptive analysis assistance has also led to the increased adoption use of executive and operational dashboards. Once regarded as a cost, IT is now viewed as an enabler of strategic and operational goals and objectives achievement. There is still prevalent use of MS Excel dashboards within some other big corporations and this is a big concern. Spread sheets are error-prone and disconnected from the rest of the organization because they are developed and maintained functionally hence limited or “none-at-all” central view of organizational performance.

However, in developing dashboards, executives, managers and other supervisors should know that in order for dashboards to succeed in driving performance improvement within the business, they must be based on the causal links that drive success for organizational objectives; communicate the desired behaviour in the organization; increase the speed, ease and accuracy of decision-making; alert decision-makers to take action; and use real-time reliable data that enables timely decisions to be made and keep objectives on track.

Today, organizations must be weary of the Velocity, Variety and Volume of data at their disposal. They must be able to separate the “disinfected” from the “infected” and use that clean data to develop meaningful and insightful dashboards. If the management team misuses dashboards, then they will fail to serve the purpose for which they are initially developed for. Some leaders have failed to benefit from the use of dashboards because they have wrongly used them to micro-manage subordinates.

It is therefore imperative and important that as managers, when you develop and cascade your scorecards and dashboards from the top to the bottom of the organization, ensure that everyone at each and every level of the organization clearly understands what it is that the organization is trying to achieve. If you are to obtain the subordinate’s full buy-in of adopting best practices and fine-tuning of strategies and business models, it is also important that you avoid using scorecards and dashboards as routine grading systems of individual performance.

Successful development of executive and operational dashboards requires the person or team tasked with the process to fully know and understand the difference between scorecards and dashboards. Most people use the terms “dashboard” and “scorecard” interchangeably as if there is no difference, but there is a difference between the two.

Scorecards, for example the Balanced Scorecard, are used at the executive level to monitor strategic alignment and success with strategic objectives and give an executive-level view into operational or functional performance. On the other hand, dashboards are used at the tactical and operational level to monitor the progress or success of tactical initiatives and operational performance on a weekly, daily or even hourly basis. Dashboards are more detailed whereas scorecards are a summarised version of dashboards.

In order to be successful at developing operational dashboards for your organization, the following approach is very important.

• Start small and make some baby steps towards the successful roll out of your dashboard implementation project. Remember, “Rome was not built in a single day”. You need to first identify and define few manageable problems that have high chances of success and clear visible effects. Once the concept has proven successful and generated interest across the organization, you can then roll out other dashboard projects.

• The business manager, not IT, should own the dashboard and lead the project, with IT as the enabler. You need to understand your needs first, generate buy-in from other groups and then work with IT to create an integrated strategy.

• Develop a model of your business processes and understand the drivers. This will help you select measures that are aligned with corporate strategy and operational objectives and because of the alignment, you will be able to experience greater impact and visibility.

• Your dashboard should only show the “critical few” metrics or KPIs. Having too many metrics will make it very difficult or sometimes impossible for you to know and understand what drives performance improvement or success. It can also be implied that you are measuring wrong things instead of focusing on the critical success factors.

• Develop a time frame or timetable for the dashboard roll out process. This is important because the quicker you develop a working system, the lesser the chances of people losing momentum and interest.

• Since dashboards are used to indicate performance, some people will view them as means to punishment if they fail to achieve the hoped-for results. As a result, you must develop a strong culture of performance that understands that dashboards are just tools to help the organization learn, improve and grow.

In summary, whether operational or tactical, dashboards are important if good managers are to stay on top of their businesses. Both scorecards and dashboards are the lever that changes culture in the organization.

Performance Measurement and Data Collection

February 22, 2012 Leave a comment

Performance measurement is all about gathering data from different sources, turning that raw data into useful information that can be used to aid decision making and improve the overall performance of the business. These days, with the advent of modern technologies, social media and a host of data sources, organisations are only a click away from consuming that breakthrough data their businesses need to outperform their peers.

Online communities such as Twitter, Facebook, Linkedin, Digg, Blogs, Internet, Email, E-commerce and other online discussion forums are connecting like minded people to share vast amounts of data and information. In other instances, you have to go out there, talk to people, observe, and listen to get the data you need. Big Data is the big thing today. Now we talk of Petabytes and as such, organisations must have the capacity and capability to analyse these vast amounts of data in a timely and cost effective manner to get insights that aid decision making.

With the need to increase sales revenues, cut costs and increase bottom-line profits, it is not surprising that most of the time, a lot of resources are wasted gathering and gathering data, some of which is useless. It should be noted that, data alone is not a driver of business. There is need for careful analysis and interpretation of that data to get information that will help answer your performance measurement related questions. Information is what drives up business performance.

There is no doubt that data collection process is a very costly and time-consuming process if not handled properly. To avoid making the repeatedly same mistake of collecting and storing data that is not serving its purpose, below are a few tips that I think businesses should consider:

• First define your performance measures; this will help you to clearly identify the data requirements for each measure.

• Find out who will be the users of the data you intend to collect and why obtaining that data is important to that user group.

• Design an appropriate data collection process and manage the integrity of the data.

• Randomly select your sample for data collection as this reduces chances of bias.

• Decide from the onset, how much in cost you are willing to spend and also whether that figure will be enough to get you all the data you need.

• Always involve the right people who are dedicated and are willing to own the data collection process.

• Be clear of how you are going to collect the data. Is it an automated process or manual process? What are the risks of using one or both methods?

• Understand that a high response rate is no guarantee to reliable data. Double check your figures and always pilot test your findings before “implementing them”. This will help you identify dark spots.

• When data collection involves key stakeholders of the business, it might be worth considering outsourcing the process for confidentiality purposes.

• Find out within your line of business or networks who else is collecting the same data that you need to avoid any duplication of efforts.

• If you are using questionnaires or forms, make sure that they are simple to understand and try to ask open-ended questions to allow the respondent to clearly express themselves.

• When storing data within your organisation, always make sure that there is easier sharing. Avoid storing data in independent systems. However, a decision has to be made of who has access to data and who doesn’t for integrity purposes.

• If possible, automate your data entry for easier detection of errors. This is also important for data integrity preservation.

• Always be aware of the time frame that you store your data before making an analysis to avoid making decisions based on out-of-date data.

By collecting the right amount and quality of data and finding insights from this data, sound decisions can be made that are critical to the ongoing success of the business.