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Building Trust in Your Data, Insights and Decisions

Over the years, the adoption of advanced analytics and data visualization tools by enterprises in order to generate key insights that can support quality business performance decisions has considerably evolved.

Nonetheless, not all investments in this arena are producing the desired outcomes.

Although significant sums of money, resources and time have been channeled towards data and analytics initiatives, the quality of insights generated, and ultimately the decisions are far from convincing.

Some of the possible reasons for explaining these failures include:

  • A sole focus on technology, and less on processes and people.
  • Misguided belief that the more data you have the better.
  • Lack of or poor organizational alignment and cultural resistance.

Delivering trusted insights is not a simple matter of investing in the right technology alone. Neither will simply collecting and storing large amounts of data lead to better decisions.

For instance, investing in state-of-the-art data analytics and visualization tools at the expense of data quality doesn’t automatically empower you to generate valuable insights that drive business performance.

Or having the right technology and quality data, but poor organizational alignment coupled with intense cultural resistance.

So how can your organization use data and analytics to generate trusted insights, make smarter decisions and impact business performance?

Prior generation of any business insights, trends or activity patterns, it’s imperative to ensure that the performance data to be analyzed is trusted. That is, the data must be fit for purpose, current, accurate, consistent and reliable.

With so much data and information available today, it’s increasingly difficult for decision makers to determine which data points are essential in understanding what drives the business and incorporate these into business decision making.

Resultantly, many organizations end up data hoarding and incurring unnecessary expenses along the process.

To ensure your data is fit for purpose, first and foremost, develop an understanding of the real business questions that the data needs to answer. By asking questions at the very beginning, you will be able to identify and define any problems or issues in their context.

Further, asking relevant questions will help you develop an improved understanding of the present factors, past events, or emerging opportunities driving the need for advanced analytics and data visualization investments in the first place.

Concurrently, asking the wrong questions results in ineffectual resolutions to the wrong business problems.

Generally, people want to kick off their data analysis effort by first collecting data and not asking the relevant questions, establishing the business need and formulating the analysis plan.

But, the quality of the insights and the value of the recommendations delivered are rooted in the quality of the data you start out with.

Data collection should only begin once the purpose and goals of the data analysis are clearly defined. Thus, the more and better questions you ask, the better your insights, recommendations, decisions and ultimately business performance will be.

Data accuracy, consistency and reliability

Ensuring that data used in analysis is accurate, consistent and reliable remains one of the common hurdles hampering analytics initiatives. One of the reasons for this is multiple data sources.

For example, suppose you want to analyze and evaluate the impact of a given marketing campaign on customer experience, in the past to do so you would have managed with data from your CRM system only.

Today, in order to get a 360-degree view of your customers, analyzing CRM data alone is not sufficient. You also need to analyze data from your web analytic system, customer feedback surveys, data warehouses, email, call recordings, social media or production environments.

Thus, you need to bring these disparate data sources together to generate valuable, actionable insights.

Issues arise when the multiple data sources define key business performance terms or metrics (e.g. sales, conversion, margin growth) differently and there is no common definition shared across the business.

This lack of shared understanding of business performance has far reaching implications on your analysis and makes it futile to compare results.

Data collection, followed by data cleansing and validation, are all therefore important. After you have collected the data to be used in the analysis per your analysis plan, it’s important to clean and validate the data to make it useable and accurate. Select a small data sample and compare it with what you expected.

Ensure the data type matches your expectations. Remember the adage: garbage in, garbage out. Even if you have the best of the breed analytics tools to slice and dice information, with poor data quality practices, your insights and decisions will remain unsubstantiated, erroneous and worthless.

For this reason and to ensure the consistency of the data used in your analysis, know and understand the source of every piece of your data, the age of the data, the components of the data, what is not included in the data, when and how often the data is refreshed.

Without data integrity, it is difficult to generate trusted insights and deliver recommendations that decision makers can confidently act on. Also, delivering trusted insights goes beyond merely attaching a colourful report in an email and hitting the send button.

Instead, the insights must be delivered in a digestible and actionable format. That is, communicated in a format that is more readily understood by decision makers in the organization. Poorly communicated insights can result in no action, negating the value of the insights in the first place. Thus, avoid getting bogged down in details.

Knowing is powerful, but acting based on knowledge is what drives smart decisions, creates true value and impacts business performance.

Converting Data Into Insights: The Right Technology Alone is No Guarantee of Success

The fact that technology is playing an increasingly significant role in executing many traditional finance tasks while at the same time generating greater insights that drive business performance is irrefutable.

However, even though many organizations are significantly investing in advanced data and analytics technologies, majority of these investments are reportedly yielding disappointing returns.

This is often because they focus mostly on the implementation of new tools, and less on the processes and people. For instance, there is a widespread misconception that data analytics is a technology issue, and also about having the most data.

As a result, many organizations are ultimately spending large amounts of money on new technologies capable of mining and managing large datasets.

There is relatively little focus on generating actionable insights out of the data, and building a data-driven culture, which is the people aspect of analysis.

Data analytics is not purely a technology issue. Instead, it is a strategic business imperative that entails leveraging new technologies to analyze the data at your disposal, gain greater insight about your business, and guide effective strategy execution.

Furthermore, having the right data is more important than having the most data. Your organization might have the most data, but if you’re not analyzing that data and asking better questions to help you make better decisions, it counts for nothing.

Reconsider your approach to people, processes and technology

The success of any new technology greatly depends on the skills of the people using it. Additionally, you need to convince people to use the new technology or system, otherwise it will end up being another worthless investment.

Given digital transformation is here to stay, for any technology transformation to be a success, it’s imperative to have the right balance of people, IT and digital skills.

It’s not an issue of technology versus humans. It’s about striking the right balance between technology and people, with each other doing what they do best. In other words, establishing the right combination of people and technology.

For example, advanced data analytics technologies are, by far, better than humans at analyzing complex data streams. They can easily identify trends and patterns in real-time.

But, generating useful insights from the data all depends on human ability to ask the right key performance questions, and identify specific questions that existing tools and techniques are currently not able to answer.

Rather than hastily acquiring new data and tools, start by reviewing current data analytics tools, systems and related applications. This helps identify existing capabilities including tangible opportunities for improvement.

It’s not about how much the business has invested or is willing to invest in data analytics capabilities; it’s about how the business is leveraging existing tools, data and insights it currently has to drive business growth, and where necessary, blending traditional BI tools with new emerging data analytics tools.

That’s why it’s critical to build a clear understanding of which new technologies will be most beneficial to your business.

Technology alone will not fix broken or outdated processes. Many businesses are spending significant amounts of money on new tools, only to find out later that it’s not the existing tool that is at fault but rather the process itself.

Take data management process as an example, mostly at larger, more complex organizations. Achieving a single view of the truth is repeatedly challenging. This is often because data is often confined in silos, inconsistently defined or locked behind access controls.

A centralized data governance model is the missing link. There are too many fragmented ERP systems. Data is spread across the business, and it’s difficult to identify all data projects and how they are systematized across the business.

In such cases, even if you acquire the right data analytics tools, the fact that you have a weak data governance model as well as a non-integrated systems infrastructure can act as a stumbling block to generating reliable and useful decision-support insights.

To fix the faltering data management process, you need to establish a data governance model that is flexible across the business and provides a centralized view of enterprise data – that is, the data the organization owns and how it is being managed and used across the business.

This is key to breaking down internal silos, achieving a single view of the truth, and building trust in your data to enable effective analytics and decision making.

Is your organization spending more time collecting and organizing data than analyzing it?

Have you put in place robust data governance and ownership processes required to achieve a single version of the truth?

Are you attracting and retaining the right data analytics talent and skills necessary to drive data exploration, experimentation and decision making?

Achieving True Cost Transformation

Suppose your business is facing significant competition from traditional and non-traditional competitors. Demand for your company’s products and services is tepid and customers are deserting you. Revenues are in sharp decline and the impact is beginning to show in shrinking margins.

To increase revenues and alleviate shrinking margins you try to cross-sell and up-sell your products and services, but all your efforts are in vain. You ultimately decide to embark on a company-wide cost transformation initiative.

Do you cut back on equipment investments, reduce marketing and IT spend, sell some of your business assets, lower inventory levels, lay off certain employees, freeze salaries, shrink transactional administrative costs or eliminate all cosmetic travel and training?

Although the above cost reduction measures are all necessary, they are basically short-term wins which are difficult to sustain in the face of new technologies, changing customer expectations and increasing competition from new entrants and other disruptors.

Cutting costs for the sake of it

Many organizational cost transformation initiatives fail to deliver lasting gains because they are often implemented in isolation without the context of the broader strategy of the business. You need to understand that cost transformation is not simply a matter of cutting costs randomly.

Rather, gainful cost transformation is linked to strategy and drives the effective execution of that strategy. In my experience, I have realized that majority of cost initiatives are more inward focused and less outward focused.

It’s all about increasing the bottom line as opposed to making sure the business remains competitive. More backward-looking and less forward-looking.

Because cost management is misaligned with business strategy, there is little focus on generating capital to fund strategic growth initiatives or diverting resources from low performing business units or unviable markets towards higher value and return opportunities.

Partly to blame for this misalignment is lack of understanding of strategy by employees across the organization. As a result, employees are unable to distinguish necessary costs (essential to meet customer expectations and deliver the organization’s value proposition) from unnecessary costs.

That is why it is important for employees across the organization to have a clearer understanding of the strategy of the business, its objectives and how these will be achieved, including the costs.

The devil is in the detail

Given that there are necessary and unnecessary costs, implementing across the board cuts will only yield marginal gains. Ultimately, high performing business areas or markets end up badly impacted because of such mediocre management decisions.

Instead of channeling resources towards investments, projects and markets which matter, or customers who matter, lackluster business performance areas tend to receive the limited resources.

To obtain customer, product or channel profitability visibility and optimize costs, you need to understand the key drivers of each. Implementing activity-based costing principles and techniques can help you answer any cost transparency and visibility questions you might have.

For instance, basing cost reduction decisions on consolidated gross margin alone obscures the reality that your business is actually making losses on particular products, customers or in certain markets.

You therefore need to drill down and understand the costs-to-serve each customer, service line or product and their drivers. This will in turn help you explain why costs are unnecessarily higher in certain business areas and make informed decisions.

Thanks to advances in technologies, companies are now able to leverage advanced analytics to analyze customer, product, market and channel data and generate insights into costs and where savings opportunities exist.

Use it or you will lose it

This culture is prevalent in organizations that are yet to break free from the shackles of the traditional annual budgeting process. During the financial year, business unit managers underspend their budget allocations.

However, towards the end of the year, to avoid losing the unused budget allocation in the forthcoming year, they willy-nilly spend the funds resulting in unnecessary costs. These funds could have been deployed somewhere for profitable return.

Unfortunately, such scenarios do little to transform the cost structure of the business and transition to a value-based model.

To avoid the culture of “use it or you will lose it” spreading over, leaders need to foster a culture of accountability, performance reporting and continuous improvement.

Hence the need to align cost transformation initiatives with strategy. Cost reduction targets must clearly be defined, both at enterprise and business unit levels, then hold leaders accountable for achieving performance improvement goals.

It’s therefore imperative to educate employees on the future financial needs of the business. They need to understand the costs that really add value in your business, and those that don’t.

Cost transformation is not a one-time initiative. Instead, it is a continuous improvement approach for leaders seeking to transform the cost structure of their companies and deliver a sustainable business advantage.

This does not necessarily mean that leaders should entirely focus on cost reduction efforts. You need to maintain an appropriate balance between achieving cost reduction targets and supporting necessary innovation and process improvements that drive effective execution of strategy and the ultimate success of your business.

Prioritizing cost optimization initiatives with only short-term goals in mind can cause unanticipated problems.

The Basics of Strategic Planning and Strategy Execution

Effective strategic planning and strategy execution are key to driving business success and growth. Unfortunately, leaders tend to focus more on the planning process and less on doing or executing.

Strategic planning is the process of articulating the vision of what the organization wants to be, defining its strategy, setting strategic initiatives, making decisions on allocating its resources to pursue this strategy, and aligning the organization to ensure that employees and other stakeholders collaborate toward common objectives.

The focus is on the future direction and performance of the organization. Through strategic planning exercises, organizations tend to produce 3-5 year rigid strategic plans documenting the organization’s strategic goals as well as action plans to achieve those goals.

Rigid strategic plans work best in a stable environment. However, times have changed. Today’s business environment is awash with substantial volatility, uncertainty, complexity and ambiguity. The abnormal is now normal and uncertainty is now certain.

As a result, enormous doubt has been cast on the effectiveness of strategic planning in the current environment, leading some to claim that strategic planning is dead.

I don’t buy this view. Strategic planning is not dead.

Yes, the environment is constantly evolving, and the organization needs to be flexible, adaptive and responsive. But, how can you address and navigate the future without a well laid plan and strategy?

In their book Sun Tzu: The Art of War for Managers, Gerald A. Michaelson and Steven Michaelson cite that:

A common mistake is to consider planning as only a mental process, an idea in our head that simply looks at the past and adjusts for the future. If your plan is not in writing, you do not have a plan at all. Instead, you have only a dream, a vision, or perhaps even a nightmare.

This is not about producing long strategy documents that very few read. Rather, it is about a producing a simple written plan that is easy to understand, such as a strategy map.

Strategy maps helps leaders define and communicate the strategy of the organization by creating a visual representation of the key business objectives on a single page. Strategy maps also outline the strategic aims and priorities of the organization and help to ensure everyone is working towards common goals.

The organization’s plan must not be rigid in nature, but flexible enough to accommodate changes in the environment or business requirements.

As a football fanatic and an avid Arsenal FC fan, I have experienced a fair share of exciting and disappointing matches. But, over the past few years, I have come to appreciate the fact that rigidity does not win matches.

Within the same match, I have watched Arsenal quickly switch from a 4-3-3 formation to a 4-2-3-1 and make substitutions depending on the realities of the match. Even though the manager had a 90 minutes’ game plan before kick-off, he also had other plans that allowed for flexibility in formations to adapt to reality.

The same approach should be adopted in business. Rather than stick to rigid planning systems that convey a message that obedience to the plan is key to business success and growth, leaders need to implement plans that allow the assessment of business performance under different scenarios.

Defining strategy and tactics

Put simply, strategy is about doing the right thing. It is about how an organization will move forward and figuring out how to advance its interests. In war terms, it is seeking victory before the battle.

On the other hand, tactics is doing things right. It is the implementation. The battle or action of the war.

However, often times there is confusion on whether strategy determines tactics or it is tactics that determine strategy.

Seeing that strategy definition is part of the planning process, and tactics is about implementation, it is safe to conclude that strategy always comes before tactics.

It is therefore important for leaders to understand that for tactics to effectively support the strategy by doing things right, the strategy itself must be right first. You must be doing the right thing. A bad strategy underpinned by good tactics can be a fast route to failure.

To do the right thing, leaders need to primarily stop focusing more on or reacting to competitors. Great strategies do not arise from reacting to competitors.

Instead, they are a product of intense discussion and deliberation that take into consideration the organization’s internal strengths and weaknesses including external threats and opportunities.

The focus should be on identifying unfulfilled customer needs or Jobs to Be Done, then devising solutions to meet those needs and ultimately assessing competitive realities to determine the viability of your strategy.

Oftentimes, the decision sequence is wrong. Leaders initially focus on profit requirements, and the decision on the needs of the market is secondary. First, you must satisfy the needs of the market. Then, and only then, can you profit from your actions.

Separating planning from execution

Innovation, profitability, and growth all depend on having strategy and execution fit together seamlessly. However, spending too much time in planning can breed indecisiveness and error.

The important thing is to get started. Unfortunately, many of us are good at thinking and bad at doing. With the right strategy, the battle is only half won. The strategy succeeds only with informed and intelligent execution of tactics.

Issues arise when planning is separated from execution. Majority of good strategies fail due to poor execution. Well thought-out plans are not followed through properly because of limited resources, managerial talent or operational skills. In some cases, it is because people are focusing on the wrong things, products or services.

To avoid poor execution of good strategies, leaders must have the ability to clearly define and communicate the strategy to employees in a format that is easy to comprehend. This is necessary for ensuring that everyone has an idea of what the key priorities of the organization are and their role in accomplishing these.

It is also important to measure, track and report on the progress of the strategy against the critical success factors of the business. This is essential for determining what is working and what is not working and make immediate adjustments to prevent further deterioration.

I welcome your thoughts and comments.

Talking About the Risks of AI and Cognitive Technologies

According to the recently published PwC’s 22nd Annual Global CEO Survey, 85% of the surveyed CEOs overwhelmingly agree artificial intelligence (AI) will have a significant impact on their business within the next five years. For this reason, they have plans to pursue AI investments.

This is despite the fact that the information gap between the data CEOs are requiring to make informed decisions and what they are getting from their teams has not closed. Lack of analytical talent, data siloing and poor data reliability are the primary reasons the data they receive is inadequate.

Nonetheless, the application of AI and the underlying cognitive technologies such as machine learning, computer vision, natural language processing (NLP), audio and signal processing, speech recognition, predictive systems and robotics are wide-ranging, with the potential to improve performance in nearly any activity that generates large amounts of data.

Highly-powered algorithms which are the basis of these computer systems are presented with large amounts of data and subjected to supervised, semi-supervised, unsupervised, reinforced and deep learning.

The goal is to train the algorithms to identify relationships or patterns between the inputs and the outputs and use those rules to predict future outcomes with input data alone.

For example, in healthcare, AI is being used to study patient clinical data and recommend diagnoses. In finance, machine learning algorithms are being used to analyze transactions and uncover fraud and money laundering.

In the retail industry, predictive algorithms are being trained to automatically group customers into various categories based on their needs or buying patterns. These insights are then used to prioritize sales efforts and tailor promotions.

In other cases, companies have piloted NLP technology to monitor social media sentiment. The technology automatically identifies conspicuous topics of consumer conversations and sentiment surrounding those topics.

The generated insights are being used to influence decisions on improving marketing and customer service.

It is no surprise then that CEOs are now exploring how to implement these new technologies in their business.

Hype-driven or well-informed investments?

As much as AI is a source of significant business opportunities, the same technology is also a source of significant threats that must be evaluated. This is essential for helping leaders make informed and intelligent investment and risk decisions.

It is foolhardy for leaders to jump on the AI bandwagon and expect to capture the promises of AI and cognitive technologies if they lack an understanding of whether, how, and where to invest in applying these technologies.

When almost everyone is talking about the opportunities of AI and cognitive systems, it’s easy to cave in to hype-driven or ill-informed investments and overlook the fact that AI and cognitive technologies are not the solution to every business problem or situation.

That is why it is critical to evaluate the business case for investing in these technologies and assess the potential impact on your company’s business model, culture, strategy and sector.

Take a holistic view of your business processes, products and markets to weigh where the use of AI maybe be practical, profitable and crucial.

Algorithms are only as good as the data they learn from

Given that AI capabilities are data-driven, closing the information and talent gaps is key to unlocking AI’s potential. AI-powered algorithms improve over time through their experience of using data.

They learn relationships between variables in historical data sets and their outcomes. The relationships are used to develop models, which in turn are used to used to predict future outcomes without needing to be explicitly programmed by a programmer.

The systems change and evolve depending on the data that is fed to the algorithms. This therefore requires the data that is fed to the systems to be accurate, complete, diverse, and free from errors and bias. If the data is incomplete, error-prone or contains innate bias, the algorithms are likely to display false patterns as well as magnify the bias leading to misleading outcomes that have far-reaching repercussions.

Since AI and cognitive technologies deliver outcomes based on historical or existing data presented to them, leaders need to acknowledge that these systems will not necessarily provide flawless outcomes.

That is why it is critical to have appropriate data governance structures and talent in place to monitor where and how these technologies are deployed across the organization.

Skilled personnel play the critical role of overseeing biases and risks emanating from algorithms. For example, these people help identify and mitigate risks associated with programming errors.

Understand the black box of AI

As business leaders lay the foundation to pursue AI investments and entrust key decision making processes to intelligent machines, it is worthy to demystify the ‘black box’ of AI.

This is the notion that we can understand the inputs and outputs of an AI-powered system, but don’t understand what happens inside.

Accountability is an important element of decision making, and in order to make AI systems accountable for their decisions, AI-based decisions need to be explainable in order to be trusted.

Rather than blindly entrust machines to make important decisions, leaders therefore need to develop an understanding of how the technology works and how it makes decisions.

Thus, business leaders must be able to identify and explain the layers of decision making which underpin the operation of the systems and influence the final outcomes.

For example, are you able to identify and explain which connections have predictive value in the multilayered deep neural networks? Although it’s impossible to analyze all the connections in a deep neaural network, it’s important to prioritize what you need to know, what you want to understand, and why.

Over time, through testing and measuring, or trial and error, you will be able to understand the thought process behind algorithms, trust the decisions they make and ensure a robust governance structure is in place to monitor these technologies as they mature.

Surge in Cyber Attacks

Big data has been a boon to the development of AI and cognitive technologies. Thanks to advances in technology, our digital lives are producing staggering amounts of data each day.

As a result, interest in AI application is surging as decision makers try to make sense of all the data at their disposal.

Nevertheless, leaders need to be aware that the more data is generated the higher the probability of cyber criminals or hackers targeting the company’s AI systems to steal personal data or business confidential information.

A major data breach can have unintended consequences that can create legal, brand and public relations issues for the business.

Therefore, as leaders seek to capture the opportunities of AI and cognitive technologies, they mustn’t turn a blind eye to the limitations of these systems.

They must also consider the various ethical, moral, and legal issues associated with the AI systems that their organizations deploy.

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