Do You and Your Organization Speak Data?

StrategyDriven Organizational Performance Measures ArticleSpeaking two languages makes you bilingual, and speaking three makes you trilingual. Any more than that, and you are a polyglot. In today’s data-driven business world, you are a data scientist if you can “speak data”.

Our world is becoming more and more about the data it generates. As pressure mounts, people who can analyze, visualize, and interpret data are becoming indispensable, much like a well-versed polyglot who can interpret and translate multiple languages with ease.

Speaking the language of data

Data surrounds us, and the ability to understand and interpret it should be a natural requirement for every individual and organization. Perhaps data and its projection on every surface of our surroundings will be the world’s new sign language. Thus, the new generation of human capital must possess this fundamental skill.

As individuals, we are challenged by the overwhelming amount of data we interact with in every scope of our lives. Learning how to make sense of data is becoming a necessity rather than a choice. If we want to continue to be part of this fascinating and engaging ecology – the world of Big Data, including the smart appliances, classrooms, schools, workplaces, and cities we anticipate in the near future – we need to be able to go beyond just speaking the language of data.

Using a data-driven strategy as a competitive advantage

It does not take a sophisticated algorithm to see the value of data scientists on today’s organizations. Clear distinctions are emerging between organizations that embody and embrace the data-driven world we live in and those who have not adapted and are still following a traditional approaches. Competitive organizations are embracing big data and re-engineering their strategies and processes accordingly.

In essence, these organizations are expanding their family of employees who are well-versed in data at every level of their managerial hierarchy. Clarity and transparency are of the utmost importance to data-driven environments where everyone speaks the language of data.

First and foremost, organizations have limited choices in today’s extremely dynamic business world. Data-driven strategies are inherently dynamic strategies that can help organizations bring the necessary transformations based on materialized and projected evidences. Data-driven strategies are also inherently granular, allowing management to sync and assess different layers of decisions and actions. Furthermore, data-driven strategies permit clear communication, responsibilities, and accountabilities at various decision layers.

Creating a data-driven culture

More importantly, the benefit of speaking the language of data allows organizations to be active in their communities and to learn through continuous engagement and feedback from their stakeholders. These are realities no organization can ignore for survival. However, in order to be competitive, organizations need to delve into the nitty-gritty of the language of data: the grammar, punctuation, and spelling that are required to be proficient in the world of big data. It not only requires passion, but also a bit of obsession.

Eloquent data speakers such as Google, Facebook, and Amazon serve as great role models for other organizations that are encouraged by the returns they see and that understand the growing need for their employees to communicate through data. This shift is not limited to creating a subset of employees who can analyze data, but to create a data-driven culture and environment that embraces all employees’ internal and external interactions as members of the big data ecology.

About the Author

Anteneh Ayanso is an Associate Professor of Information Systems at Brock University’s Goodman School of Business. He is certified in Production and Inventory Management (CPIM) by APICS and teaches and researches in the areas of data management, business analytics, electronic commerce, and electronic government. Anteneh Ayanso can be contacted at (905) 688-5550 x 3498 or [email protected]

Four Phases of High-Quality Business Performance Assessments

Business performance assessments are conducted in a series of phases: Identify, Plan & Schedule, Execute, and Close-out. Associated with each phase is a collection of principles, best practices, and warning flags aiding the identification, communication, and acceptance of value-adding, self-critical performance improvement opportunities.

Assessment Phases

  • Identify Phase: The Identify Phase starts the business performance assessment process by defining the broad parameters within and by which the assessment
    will be performed.
  • Plan and Schedule Phase: The business performance assessment process continues with the Plan and Schedule Phase during which the specific assessment activities – document reviews, personnel surveys, activity observations, and individual interviews – to be performed are identified and scheduled.
  • Execute Phase: The Execute Phase is at the center of the business performance assessment process. During this phase, assessors gather and analyze data from a number of sources to identify performance improvement opportunities.
  • Close-out Phase: The Close-out Phase marks the end of the business performance assessment process. Performance improvement opportunities are captured within the corrective action program and assessment documentation is properly cataloged.

As illustrated by StrategyDriven’s Information Development Model, business performance assessments belong to the third tier of performance data refinement. Performance reports at this level benefit from human intelligence added to supporting data during: initial data synthesis, basic trend identification and analysis, multi-trend synthesis, and basic model application. It is the infusion of human knowledge and experience at these points that makes these assessments broadly integrated and highly insightful.

To learn how to maximize the value of your business performance assessment efforts:

About the Author

Nathan Ives, StrategyDriven Principal is a StrategyDriven Principal, and Host of the StrategyDriven Podcast. For over twenty years, he has served as trusted advisor to executives and managers at dozens of Fortune 500 and smaller companies in the areas of management effectiveness, organizational development, and process improvement. To read Nathan’s complete biography, click here.

Lost in Translation

Increase the impact of customer insights and analytics. How to break down the barriers between the analytics community and the business.

Data is omnipresent and within our grasp yet business truths are still elusive. Finding meaning in that data requires sifting through droves of extraneous information for business insight. It means distilling raw information into a “story” about our customers, our business growth levers, and the business challenges we face – all with a view of moving the business forward.

To extract this information, businesses have found growing within their midst an “analytics community” – groups of data crunchers clustered in back rooms, mining information warehouses and marketing databases for – what exactly? The answer is not always clear, because the value of the information is not yet fully realized or leveraged.

Over the past two decades, data-rich companies have found their analytics teams playing the role of internal service providers. They are tasked with mining through data to find answers to particular business problems. These people – the statisticians, database marketers, modelers, programmers, market researchers and analysts of all stripes – are the people who sit between an organization’s mushrooming information sources (databases, market research studies, marketing campaign analytics, predictive modeling results … the list grows infinitely) and a business output.

But it’s no longer enough for analysts to stay within their silos of expertise and crank out analysis. The analyst community cannot measure its worth by how quickly reports are delivered, or how happy those insights make the people who ask for them. The analytics community must emerge from the service provider mindset and into one of driving business success. Analysts have the privilege and the obligation to ensure that their organizations fully leverage the power of their corporate data banks to propel the business forward.

The translation layer
For large organizations with many lines of business and deep, rich databases, making sense of information has become a business itself. What is needed now is a “translation layer” to ground businesses in fact-based decision making.

The analytics community is ideally positioned to become the translation layer. They have the skills to see the whole picture where everyone else sees only parts of the puzzle. They can provide clarity on strategic issues. But first, they need to move out from the back room and connect the dots across the business to ensure the puzzle makes sense, and how, within the context of the organization’s strategy, its data could be put to maximum use. This is when analysis evolves into insight and when businesses are able to compete on analytics.

From service provider to business driver
As data becomes increasingly central to organizations – and a key business enabler – the analytics community needs to evolve from service providers to business drivers. It is no longer enough to hand over answers to small, narrowly defined business problems. Analysts must work to become the essential “translation layer” between the wealth of an organization’s insights and profitable business applications.

Here are some ways to make that happen:

Better business knowledge
The analytics community must step outside of their silos of expertise to better understand the business overall. They need broader exposure to business strategy and priorities, business and financial performance, and market context. This means investing the time to help them better understand how the business makes money so that they are in a better position to support greater business growth and move the business forward. Investing the time to train and develop this knowledge base will change the kind of insights that are generated – and increase the value the analytics community can bring to the table.

Better return on insights
Analytics teams must align requests to the strategic priorities of the organization. They need to look both at how the business will benefit from where time is spent, and at the opportunity cost of NOT spending time in places where it will yield greater returns. Treating analytical resources like marketing dollars will help ensure wise investment.

Clear explanation of results
Analysts need to make connections across the insights team to fully understand problems and opportunities within a broad, full-picture context. They must reach out to analytical teammates (in modeling, database marketing, research, finance) to connect the unconnected. Insights must be thoroughly rendered and clear, making them easier to understand and act upon. Time must be invested for analysts to become better communicators (both written and verbal) to improve information clarity.

Moving forward
Business analytics teams love digging through data to discover statistical patterns that inform a problem. Executives are hungry for fact-based solutions to their business challenges. Follow the suggestions in this article to pair these two groups successfully – and improve your organization.

This article was republished with the permission of sascom Magazine.

About the Author

Lori Bieda is the Executive Lead for Customer Intelligence Solutions across the Americas for SAS. Prior to SAS, she was Vice President, Client Insights and DB Marketing at Canadian Imperial Bank of Commerce (CIBC) and was responsible for the creation of marketing and analytics strategy for the bank.

Eight Levels of Analytics

Not all analytics are created equal. Like most software solutions, you’ll find a range of capabilities with analytics, from the simplest to the most advanced. In the spectrum shown here, your competitive advantage increases with the degree of intelligence.

Answer the questions: What happened? When did it happen?
Example: Monthly or quarterly financial reports.
We all know about these. They’re generated on a regular basis and describe just “what happened” in a particular area. They’re useful to some extent, but not for making long-term decisions.

Answer the questions: How many? How often? Where?
Example: Custom reports that describe the number of hospital patients for every diagnosis code for each day of the week.
At their best, ad hoc reports let you ask the questions and request a couple of custom reports to find the answers.

Answers the questions: Where exactly is the problem? How do I find the answers?
Example: Sort and explore data about different types of cell phone users and their calling behaviors.
Query drilldown allows for a little bit of discovery. OLAP lets you manipulate the data yourself to find out how many, what color and where.

Answer the questions: When should I react? What actions are needed now?
Example: Sales executives receive alerts when sales targets are falling behind.
With alerts, you can learn when you have a problem and be notified when something similar happens again in the future. Alerts can appear via e-mail, RSS feeds or as red dials on a scorecard or dashboard.

Answers the questions: Why is this happening? What opportunities am I missing?
Example: Banks can discover why an increasing number of customers are refinancing their homes.
Here we can begin to run some complex analytics, like frequency models and regression analysis. We can begin to look at why things are happening using the stored data and then begin to answer questions based on the data.

Answers the questions: What if these trends continue? How much is needed? When will it be needed?
Example: Retailers can predict how demand for individual products will vary from store to store.
Forecasting is one of the hottest markets – and hottest analytical applications – right now. It applies everywhere. In particular, forecasting demand helps supply just enough inventory, so you don’t run out or have too much.

Answers the questions: What will happen next? How will it affect my business?
Example: Hotels and casinos can predict which VIP customers will be more interested in particular vacation packages.
If you have 10 million customers and want to do a marketing campaign, who’s most likely to respond? How do you segment that group? And how do you determine who’s most likely to leave your organization? Predictive modeling provides the answers.

Answers the question: How do we do things better? What is the best decision for a complex problem?
Example: Given business priorities, resource constraints and available technology, determine the best way to optimize your IT platform to satisfy the needs of every user.
Optimization supports innovation. It takes your resources and needs into consideration and helps you find the best possible way to accomplish your goals.

The best analytics for your business problem
The majority of analytic offerings available today fall into one of the first four areas, which report historical data on what happened in the past but no insight about the future. For simple business problems, these analytic solutions will be all you need. But if you’re asking more complex questions or looking for predictive insight, you need to look at the second half of the spectrum. Even better, if you can learn to use these technologies together and identify what type of analytics to use for every individual situation, you’ll really be increasing your chances for true business intelligence.

This article was republished with the permission of sascom Magazine.

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StrategyDriven Podcast Special Edition 38 – An Interview with Robert Morison, co-author of Analytics at Work

StrategyDriven Podcasts focus on the tools and techniques executives and managers can use to improve their organization’s alignment and accountability to ultimately achieve superior results. These podcasts elaborate on the best practice and warning flag articles on the StrategyDriven website.

Special Edition 38 – An Interview with Robert Morison, co-author of Analytics at Work explores how to leverage analytics to make better business decisions that ultimately lead to superior business results. During our discussion, Robert Morison, co-author of Analytics at Work: Smarter Decisions, Better Results shares with us his insights and illustrative examples regarding:

  • the tangible benefits leaders realize as a result of incorporating analytics in their decision-making process
  • balancing the art and science of decision-making and recognizing when the process is out-of-balance
  • types of questions analytics can help answer
  • the five stages of analytical maturity
  • the five key analytics DELTA components: Data, Enterprise, Leadership, Targets, and Analysts

Additional Information

In addition to the invaluable insights Robert shares in Analytics at Work and this special edition podcast are the resources accessible from his website,   Robert’s book, Analytics at Work, can be purchased by clicking here.

About the Author

Robert Morison is co-author of Analytics at Work. For the past twenty years, Robert has led breakthrough research at the intersection of business, technology, and human asset management. He has written or overseen more than 130 research and management reports on topics ranging from business reengineering to electronic business to workforce demographics. Robert is co-author of three Harvard Business Review articles and Workforce Crisis: How to Beat the Coming Shortage of Skills And Talent. To read Robert’s complete biography, click here.