How to Make the Most of Data Visualization

The human brain can process entire images that the eye sees in as little as 13 milliseconds. 

Regardless if you are the CEO, technology director, or compliance officer within your organization, information in the form of graphs and charts is not only easier to digest, but also promotes data-driven decision-making.

But there is a problem: Most organizations generate massive amounts of organizational data every day that is left unused, taking up storage in its rawest form. Consequently, teams are unaware of the data available to them, and if they are aware of it, they are unsure how to access or interpret it. Scattered and disorganized data requires hours of manual consolidation, cleansing, and validation, and the output is ultimately prone to manual errors. 

If your teams are inundated with spreadsheets and spending an inordinate amount of time on gathering, cleansing,  and reconciling disparate data sources manually rather than providing value-add analysis, then it might be time for a change.

If your organization’s leadership is not leveraging organizational data as a valuable asset to drive proactive risk mitigation and decision-making, then the real question is, “How much am I spending by NOT investing in my data?” 

The cold, hard truth is that organizations can no longer afford to rely on spreadsheets and dirty data to make business decisions; however, data visualization can help to automate the consolidation and aggregation of data, equipping teams with the power to quickly interpret information to drive business results and increase overall team efficiency and satisfaction.

What is Data Visualization?

Before we cover how visualizing your data can help your organization, you may be wondering, what is data visualization? 

Data visualization is the transformation of unstructured or raw data into a visual form to communicate complex data relationships and data-driven insights in a way that is captivating and easy to understand. By succinctly summarizing copious amounts of organizational information into visually appealing reports, teams do not have to dissect and analyze underlying data to understand trends over time.

Data visualization bridges the gap between data and action by providing access to real-time metrics, allowing businesses to be better positioned when it comes to: 

In addition, data visualizations provide leaders the opportunity to harness existing data and leverage it to learn from past mistakes, build on past successes, and anticipate developments that drive innovation and accurately predict future outcomes. 

Key Benefits of Data Visualization

The average organization collects data across 400 different sources. However, about 47% of that data goes completely unused because it is disorganized, unstructured, and dirty, which can cost your organization countless hours and dollars. 

In order to fully realize the value that your data can offer, good data visualization is imperative. However, investing in data visualization tools and technologies without organizational buy-in or foundational data practices can actually prevent companies from maximizing ROI in the long run. In order to ensure sustainable value realization, you must first establish core data governance practices, clean up data sources, and determine the data needs of your organization.

Once these foundational data practices are in place, data visualization can deliver the following key benefits:

1. Increased Comprehensibility of Data and the Break Down of Data Silos

Because visual data is processed much faster by the human brain, presenting data in an easily consumable format has the incredible ability to streamline organizational production. In contrast to text form, which has more historically been used as the preferred medium for exchanging information, humans can process visual images 60,000x faster. Furthermore, data visualization provides a much more interactive approach to displaying data, thus allowing users to quickly understand the story the data is telling without needing words to provide context. Presenting data to your executives or teams in a visual manner allows for far fewer gaps in communication throughout the enterprise, which can ultimately shorten business meetings by 24% and give you and your teams more time for other value-add initiatives.

Additionally, data visualization can break down data silos within your organization and reduce the amount of time spent on manual reporting. Sixty percent of employees believe they could save six or more hours if static reporting was automated. Business Intelligence tools bridge the gap between siloed data and reporting by utilizing centralized data to display accessible visual reports. Ultimately, implementing a centralized Business Intelligence solution can help prevent wasted efforts on non-value-add activities, while also acting as a catalyst for cross-functional collaboration. 

2. Save Costs & Drive ROI

How can visualizing your data really drive a return on investment? The answer to this question is unique to every organization and depends on the problem you are trying to solve; however, the competitive advantages to investing in Business Intelligence are as follows:

In addition to understanding the tangible benefits of implementing a Business Intelligence solution, it is equally important to note the true costs of not having one. How much will a lack of visibility, process inefficiencies, employee unproductivity, and outdated IT enhancements cost your organization over time? Some potential costs to consider include:

While ROI looks different for every organization, statistics show that data visualization offers an average of $13.01 ROI on every dollar spent. Business Intelligence tools make your data centralized and easily accessible, so employees spend more time on business functions rather than compounding the problem large amounts of data can present.

Data Visualization Best Practices

The quantitative and qualitative benefits of implementing Business Intelligence tools are endless; however, to fully capitalize on your investment in data visualization, you will want to consider these five data visualization best practices:

1. Identify Your Most Critical Data

The first best practice is to establish a core set of data that is most relevant to the entire enterprise. By first defining the business impact you are striving to achieve from implementing data visualization, you can then identify your most critical data element. Are you hoping to:

Once you identify your most critical data elements, you can begin to strategically and actively reduce the volume of data you do not need and acquire new data elements to paint a holistic picture of your organization.

2. Establish Data Governance

Establishing data governance to aggregate and organize by effectively managing data definitions and values is imperative to lay the foundation for sustainable value realization from an investment in data visualization. A few key first steps include:

But these steps are only the beginning. Keep in mind that data management requires ongoing evaluation of data quality to best promote accurate reporting.

3. Implement a Centralized Data Model 

In order to blend data sources into cohesive visualizations, it is best practice to create a centralized data repository. ​​Whether the aggregation occurs within a reporting tool itself or reporting database, it is imperative to blend data sources to provide cross-functional reporting. Benefits of having a centralized data model include:

4. Create a Data-Driven Culture

A key consideration of any digital transformation is to ensure employees within the organization embrace the new technology. In order to increase adoption and combat any resistance to change, it is essential to develop a cultural framework that motivates your employees to leverage the Business Intelligence tools available to them. Of course, this is easier said than done. Today, only 24% of companies admit to having a truly data-driven culture. A few challenges to overcome include:

In spite of these challenges, a data-driven culture is possible to achieve. You set yourself up for success when:

5. Know the Audience

Another data visualization best practice is to know your audience. When designing reports, it is important to understand who is the intended audience of the report and what information the end-user needs. For example, executive-level audiences will require a different level of granularity than employees completing day-to-day tasks. A few dashboards to consider to variate data visualization for different audiences include the following:

Rather than just displaying information that was previously in PowerPoint into a Business Intelligence tool, you can fully harness the power of data visualization by asking questions such as:

From there, you can design the right reports by leveraging data to surface actionable insights and improve business performance.

Success Stories: Implementing Data Visualization Into Your Organization

While there is no one-size-fits-all solution when it comes to visual analytics, at Kenway Consulting, our expertise and obsession with all things data have helped us paint the picture of transformative business opportunities for organizations just like yours. Here are a few examples of how Kenway has used data visualization to help small businesses to large, global enterprises alike.

Gain Insights From Untapped Data

A mobile application company developed an app focused on virtual engagement to provide cultural institutions with enhanced experiences for their visitors. The application collected large amounts of data from its users but had no way to make that data insightful for clients. The organization was looking for an analytics platform that could:

Kenway developed a fully automated, end-to-end process to support the visualizations needed to help the client’s customers understand the value of their data and make informed decisions. The reports created provided insight into who was visiting their institutions, where visitors were spending most of their time, the most-visited areas of the property, and more. 

Read the full case study here.

Establish a Single Source of Truth 

A leading asset management firm had the goal of harnessing massive amounts of data to become more strategic and intentional in targeting its wealth advisor clients. However, due to numerous data inefficiencies and process gaps across the organization, it struggled to support its sales teams in understanding the full breadth of their relationships with current and potential clients. The company lacked real clarity around advisor profiles such as:

The main problems faced by the organization included:

      1. Siloed data sources
      2. Disconnects in organizational communication
      3. Slow and ineffective processes

Kenway collaborated with the business to understand its needs, analyze the current state, and work with technology teams to build a design that would deliver results. To ensure the organization was set up for success and continued growth, we took the unique approach of working together with the asset management company, as opposed to helicoptering in and leaving them with a design and recommendations that were not tailored to their needs. 

This partnership also allowed the asset management company to cultivate institutional knowledge and build in-house capabilities and data visuals needed to support and adapt the modern data platform over time. This focus on enabling critical business outcomes built upon a solid baseline of governance and architectural capabilities helped to ensure sustainability and long-term success.

Read the full case study here.

Visualize Forecasting Data

When it comes to your business, it is better to be proactive rather than reactive. While we cannot predict the future, business forecasting can help you prepare for potential outcomes. Data visualization can be especially helpful in the development of forecasting charts. 

Forecasting charts analyze your descriptive analytics (historical data) over a specific period of time and provide predictive analytics, or trend lines, that extend past the current date to help you predict future business outcomes. Predictable forecasting can be beneficial when trying to:

At Kenway, we offer Business Intelligence solutions like Power Business Intelligence to bring your business-critical insights to life through customized reports and dashboards. Using applications such as What-If Analysis, your organization can plan for best-case and worst-case scenarios over the next 6-12 months. 

Data Visualization
By leveraging historical data as a proxy for future Inventory Aging, we created a dashboard that forecasts inventory aging for a retailer client to predict the concentration of aged inventory for future risk mitigation.
data visualization methods
Using historical revenue trends as a proxy for future revenue predictions, we created a dashboard that allows the finance team to gain insight into future company performance.

Drive Revenue by Increasing Timeliness and Accessibility of Customer Data

Data visualization can help your organization have better insights from customer data to quickly identify and capitalize on new market opportunities.

In order to identify new customers in new markets, you first need to have a strong understanding of your current customer base. Aggregating and cleansing customer data that is spread over a range of disparate sources, such as sales, accounting, and marketing, can be extremely time-consuming and near impossible through conventional methods and excel spreadsheets. Even if you manage to combine various data sources, surfacing meaningful insights based on criteria such as product line, region, demographic, or sales territory can prove to be even more difficult.

Blending disparate customer data within a Business Intelligence tool allows you to create standardized KPIs, metrics, and visuals to better analyze the characteristics of your current customer base in real-time and become more intentional and strategic with your go-to-market strategy. 

Kenway has vast experience in leveraging analytics and data visualization to reveal a 360-degree view of your customers. To make this information even more powerful, Kenway can also blend external and internal datasets to present a macroeconomic view of your internal data trends.

data visualization best practices
Leveraged CRM data to forecast future opportunities based on the expected probability of current sales targets to materialize. 
Data visualization employee interaction map
Leveraging CRM data, we created a network map to show the prospecting synergies across the sales organization for more intelligent targeting.

No matter how savvy your sales organization and business leaders are, their innate ability to identify new opportunities does not compete with a tool that can quickly analyze and consolidate terabytes of data.

Selecting the Best Data Visualizations For Your Organization

There is no denying it: Enterprise data collection is not slowing down. In fact, over the next two years, it is expected to increase at a 42.2% annual growth rate. As the volume and complexity of data caches continue to proliferate, Business Intelligence and data visualization tools will enable your entire organization to consume the information being collected and make proactive business decisions.

Not sure how to navigate the future of your data? Kenway can help. From surveys and polls to decision support tools for the C-suite, our Power Business Intelligence portfolio highlights how our Business Intelligence engagements have helped transform data into consumable, interactive dashboards and reports that drive business-impacting decisions. Request a free data strategy consultation today.

 

Making Data Insightful

 

Industry: Technology

Solution: Data Warehouse and Data Analytics

Client: Virtual Engagement and Mobile Application Company

The Situation

An organization focusing on virtual engagement provides cultural institutions with enhanced experiences for their visitors by turning mobile devices into personal concierges and expert tour guides and providing options for augmented reality and virtual reality experiences at zoos and parks. In the process, the mobile app collects invaluable usage data from visitors such as time spent in an exhibit, videos watched, paths taken through buildings, and other key indicators that can then be analyzed to make strategic decisions around marketing, value of exhibits, and areas of improvement. The company is setting a new standard for virtual and mobile experiences at cultural institutions and needs to ensure that the data it collects can be monetized and leveraged by its customers.

The Problem

The application collected large amounts of data from users but lacked an effective way to make that data insightful for clients. The organization was looking for help identifying analytical insights from its aggregate user data (such as exhibit engagement patterns, high traffic areas, visitor demographics, etc.) that would be powerful enough to support strategic decision-making and could be sold back to these cultural institutions.

The organization was also interested in understanding what the Business Intelligence (BI) landscape could offer, and what tools were available to help continue building out its analytical framework.  Specifically, they wanted to know more about:

The Solution

Kenway provided a mix of services to build a solution that uniquely met the needs of this organization, including Vendor Assessment, Data Management, BI, Architecture and Design, and Custom Development. Ultimately, Kenway worked to retrieve the data collected through the app and load that information into a newly-built Redshift backend database.  They also wrote APIs to pull all data into staging tables, SQL scripts to pull that data out of the staging tables and into a normalized data model, and a Qlik Sense reporting tool to visualize the data.

 To determine the best BI tool on the market, Kenway performed a vendor assessment comparing different BI tools on the Gartner Magic Quadrant; Tableau, Qlik Sense, Power BI, and Amazon QuickSight (not on the quadrant) were all considered. Based on an assessment of the organization, Kenway knew the tool would need to provide an end-to-end process of source to dashboards, an ability to handle larger volumes of data/scale to support big data, and easy-to-use, intuitive visualizations.

As an aid to this assessment, Kenway’s BI expert also created a vendor assessment to weigh and analyze features offered by each of the options being considered. The paper highlighted key areas of importance such as stress test, strength and weakness deep dive, total cost, and logistics and implementation. After all comparisons were finished, the recommendation came down to Qlik Sense and Power BI, with both having similar features to meet the client’s needs.  Once cost and integration factors were considered, Qlik Sense was identified as the best tool to deliver on the client’s defined requirements.

To bring the application usage data into insightful visuals, Kenway provided a combination of Application Development, Data Management, and Analytics services to further expand the capabilities of its client’s existing Amazon Web Services (AWS) architecture. They used the AWS pipeline to execute SQL and take the data from a staging table to the production table. By providing the right technical skills, Kenway was able to develop a fully functional “Analytics Pipeline” to bring the data into a data warehouse and make it available for the analytics tool. This new data warehouse was built on Redshift. To help enrich the demographic data of the users, a third-party data source was brought in to merge with the client’s app usage data. The demographic data was provided monthly through an SFTP site that Kenway automated to retrieve, load and merge to its client’s data set. This additional data source provided more insightful analytics to the customers.

What We Delivered

Kenway delivered a fully automated, end-to-end process that pulled the app data already being stored on AWS using APIs, loaded it to a normalized data model on the newly-built Redshift data warehouse, and visualized it using Qlik Sense BI reports.  The end-to-end solution included the following:

The Result

Kenway developed a fully automated, end-to-end process to support the visualizations needed to help the client’s customers understand the value of their data and make informed decisions. The reports that were created provided insight into who was visiting their institutions, where visitors were spending most of their time, most-visited areas of the property, etc.

If you’d like to learn more about how Kenway can help with your Analytics Pipeline or our custom development expertise, reach out to us at info@kenwayconsulting.com.

 

A few examples of the visuals that were created:

Visual 1 - demographics:

Visual 2 app usage summary:

Visual 3 – app openings:

Visual 4 – traffic patterns:

Visual 5 – favorites within the app:

Visual 6 – videos watched within the app:

 

 

Top Innovations from Snowflake Summit 2021

Over the past year, Kenway has continued to invest in its partnership with Snowflake through a variety of experiences and certifications such as Snowflake SnowPro Core. Snowflake is a cloud data warehouse that unites siloed data, discovers and securely shares data, and executes diverse analytic workloads. We believe there are many opportunities for our clients to reap the benefits of this innovative cloud warehouse and data lakehouse platform.

As one of Kenway’s certified Snowflake SnowPro Core employees, I recently had the opportunity to attend Snowflake’s two-day virtual summit, which provided me significant insights into all the new features they will soon be releasing to the public. Here are some of the overall themes and highlights that were discussed:

Connected Industries

Every Snowflake user can access data across the cloud, regardless of region, which provides organizations a better means for data collaboration. Some companies are using Snowflake to bring in their data in real time, which allows them to get up-to-date insights on transactions. Snowflake has substantially increased the data sets available on its Data Marketplace. The Marketplace is where companies can share their data rather than make copies of it and then move it around. Another key topic in the Connected Industries domain was Snowflake’s new integration with ServiceNow. This native integration allows the ServiceNow data to be readily available in Snowflake for companies to manage consumer relationships and become part of a central data repository, rather than being siloed.

Another powerful feature in Snowflake that helps industries stay connected is its improved data sharing capabilities. There are three methods for organizations to share data: share it with other accounts, publish to a private exchange, or publish to the public marketplace. The new API will help guide users through the process to make entire databases, tables, views or functions available to those with whom they’d like to share. Snowflake sharing eliminates the need to create and build processes to move data inside or outside of an organization. There is one copy of the data created, and data updates are made available to consumers in real time.

Global Governance

Last year, Snowflake released a feature that allowed users to mask data dynamically. To add onto that, they have been working on row access policies and object tagging. Personally identifiable information (PII) is difficult and tedious to identify in data, and is usually done manually. Snowflake now has two key features that allow its customers to tag data automatically through classification and anonymized views. They can then apply functions to the table that will either generalize or suppress that data. These features still provide the analytical value needed to generate insights and protect customers’ information.

Security has also been front and center when talking about storing data in the cloud. To address this concern, Snowflake has a layered security model that includes network security, identity authentication and access management, and single sign-on. One of the latest improvements is the private connectivity to users’ Snowflake internal stages. The staged data that is accessed through client apps remains on the private network. On AWS, users access Snowflake’s S3 PrivateLink capability where the data stays on the Amazon private network which eliminates the need for proxies. Azure has a similar capability called Azure Private EndPoint. Another important security feature is the new session policies that can be set at the account level or individual users, as well as database and UI timeouts.

Platform Optimization

Snowflake continues to improve its performance and efficiency, and the most recent updates made are better compression and query acceleration. All new data being written to a warehouse is compressed even more, resulting in some customers benefiting from a 30% cost savings for user storage. The query acceleration service boosts performance which can result in 15 times performance improvement and less latency, giving users better predictability on query response times. These new features are being done behind the scenes so that there is no impact or downtime to Snowflake users. In conjunction with performance and storage improvements, users will soon have a new administrative experience with improved admin screens to better track usage, storage and costs for their organizations.

Data Programmability

Snowflake users have been utilizing task scheduling to help with their data pipelines. One improvement to this feature is serverless tasks which allow for serverless execution, and automatically determine the appropriate amount of compute resources. Another new feature in this domain is schema detection which can determine the schema for semi-structured file types of Parquet, ORC or Avro. Users will soon also be able to store and process files as they do with structured or semi-structured data, eliminating the need to load data from files in the data lake. Developers will now have the ability to create functions and stored procedures in a SQL based language rather than just JavaScript.

Another new feature built to make developers’ lives easier was Snowflake’s SQL API. This allows them to submit SQL calls through the API, which supports standard queries, DDL and DML statements. These API calls are lightweight with little overhead. Why is this important? If organizations migrating their applications use Snowflake as their data warehouse, there will be no need for them to refactor the code since Snowflake already includes REST API. This key benefit ultimately helps reduce migration costs.

There were many more great discussions at Snowflake Summit 2021, all of which are now available to watch on-demand. We’ve already begun to share some of these insights with our clients, and look forward to helping them leverage these capabilities.

If you attended the Summit or have a chance to watch the recorded sessions, I encourage you to share your thoughts and favorite takeaways. Please connect with me on LinkedIn to discuss what you found most interesting – I’d love to compare notes!

 

Introduction to Data Governance

In today’s age, the generation of large amounts of both structured and unstructured data has expanded at an exponential rate, along with the complexity of data ecosystems. Key data sources are increasing in both size and volume, and the way that data is captured and assessed is shifting from strictly on-premise databases to cloud technology. Data has become critically valuable to every industry and department whether it be financial services, data science and analytics teams, sales and marketing, or healthcare. Organizations are becoming more reliant on data to run day-to-day operations and drive decision-making. In order to keep pace with the ever-expanding wave of source systems, digitization, and Big Data, organizations are making Data Governance an increasingly significant institution that is vital to both business and information technology (IT) strategies.

Read More: Introduction to Data Governance - White Paper_(PDF)

 

The CDO’s toolbox: Data Governance & Data Management

Until the early 2000s, most firms accumulated data at a manageable rate and were able to collect, store and use that information with little additional effort. However, over the past two decades, the introduction of automation mechanisms (i.e., robotics, IoT, etc.), social media, and cloud storage has made it increasingly cheap and easy to collect and store data.

Today, organizations are accumulating an unsurmountable amount of information. Innovative academics have identified a plethora of ways to leverage data, and tech giants have developed (virtually) limitless computing power to process it. The world is now exponentially accumulating information so fast and vast, that we’re observing instances where laws are struggling to protect it and companies are frantically trying to leverage it.

As one would expect, with an increase in the amount of available data comes an increase in usage across organizations. From executives and employees, to customers and regulators, it’s become a vital component of interaction across a myriad of stakeholders, making it extremely critical for competing in today’s emerging data-driven economy.

Data as a Strategic Asset

This increase in data usage has led us to a world where the quality of data matters. When the data that is stored has inconsistencies in form, has missing components, or is not up to date it can have a significant impact on an organization.

A colleague shared with me an example of this quality issue occurring during her previous job at a global bank. When generating monthly reports that showed details about funds (i.e., returns, exposure by geographic region, product type, currency, etc.), she found herself spending a ton of time exporting data from systems, making manual adjustments so that it was in the correct form, realizing it didn’t look right, going back to the numbers to figure out what was wrong, and so on.

In such situations, poor data quality can turn out to be costly because analysts and managers often find themselves spending more time preparing data than analyzing it. Unlike a decade or two ago, today, poor data quality has a direct and greater financial impact. Governing and managing it inadequately is often indicated by subpar operational performance (i.e., bad decisions due to incorrect reporting), reputation loss (i.e., data leaks), or worse (i.e., regulatory fines due to non-compliance with privacy laws).

Data Governance and Data Management Lay the Foundation

To thrive in today’s data economy, the CDO/CIO office is often pressured to be intentional about continuously improving data quality across the organization. To do this, they need to rely on both Data Governance and Data Management mechanisms. While the concepts of Data Governance and Data Management are commonly understood and documented, one of the major differences between the two is that Data Governance is a strategy (i.e., macro) and Data Management is a practice (i.e., micro). But both are necessary for any organization to thrive.

Organizations that formalize Data Governance have roles and responsibilities defined for data ownership and stewardship. They also have policies, procedures and enforcement in place to ensure that data quality standards are upheld from data entry to data delivery. On the other hand, Data Management is prevalent in organizations that have tools and technologies that serve various purposes, such as visibility into metadata (i.e., what data is in which table, measure calculations, data lineage, etc.), access controls, etc.

Examples of common problems solved by Data Governance and Data Management:

Both Data Governance and Data Management are complementary in nature and essential for an organization to run optimally in today’s world. In the ideal state, a combination of these two would allow organizations to understand the positive effects data can have on their business and offer the ability to create that impact with little effort. Such an ideal further enables organizations to connect the right insights with the right people for driving value all the way from optimizing business processes to propelling innovation.

Driving Value from Data Governance and Data Management

Structuring and initiating Data Governance and Data Management implementations can appear daunting and intimidating. Common trends suggest organizations often struggle to launch and sustain a Data Governance program, or lack buy-in on basic Data Management tools because it seems to be an expensive proposition.

Kenway’s approach to data is different. By understanding and prioritizing based on use cases supported by business objectives, organizations are enabled to structure and pace the development of their data infrastructure in such a way that the project could potentially pay for itself by adding immediate ROI to business initiatives.

We’d love to learn more about your organization’s Data Governance and Data Management initiatives. Drop us a note at info@kenwayconsulting.com, or check out our Information Insight page to learn more.

Request a Data Governance Consultation

 

 

Cash is King – Managing Better with Revenue Forecasting

It is a truism that in small business, “Cash is king!” Given that as a baseline, revenue forecasting, or forecasting in general, is crucial for your organization to accurately manage costs, understand what strategic initiatives are advisable, and understand when you can and should expand your team.

But predicting the future is hard. While it would be great to have a crystal ball when planning for your business, most of us will have to make do with “What-If” scenarios based on educated guesses and estimated probabilities. Forecasting this way with any sort of accuracy is painstaking, and often requires you to rerun reports and redo manual manipulation in Excel as soon as someone asks, “What if X happens?” This is both costly from a time perspective and prone to error because the more manual manipulation, the greater the chance that there will be a typo or fat finger error.

Fortunately, there’s a better way. At Kenway, we haven’t found a crystal ball (yet), but we’ve created what could be the next best thing: a dynamic revenue forecasting model in Power BI with built in “What-If” parameters.

Our new Revenue & Resource Insights and Optimization service uses your organization’s existing OpenAir data to project future revenue. You can then rapidly run scenario analysis by changing inputs such as additional projects, hours, bill rate and fixed fee revenue to help drive planning and see how forecasted revenue will shift up and down for each scenario.  By instantly marrying the flexibility of an Excel “What-If” model with the up-to-date forecast data from OpenAir, you save both time and headache while getting a more nuanced view of your organization’s future state.  That is all super important for cash management.

But wait, there is more …

Revenue forecasting has other benefits beyond ensuring your business has liquidity. It also has the added value of leading to a deeper analysis of your customers and products. If you are forecasting that a long-time client is going to grow significantly more than historical trends, that is worth additional analysis. Is your business growing with this client because they are growing, or are you getting better at identifying and delivering value? Similarly with your products, if the results of your analysis show that you are selling more product, do you know why? Can you accelerate even more? What are you doing well, and can that inform what you do more of and where you make future investments?

We at Kenway believe that unless you are leveraging your data to understand what is next, you are leaving out an important part of protecting and growing your business. Intrigued? If so, check out the Revenue and Resource Insights and Optimization solution on our website, or email info@kenwayconsulting.com to learn more. 

 

COVID-19 Interactive Report

As of March 11, 2020, WHO officially declared COVID-19 (Coronavirus) a pandemic. Amidst this global outbreak, people are bound to experience feelings of fear and anxiety. We at Kenway believe that in circumstances like these, the more information the better, which is why we have developed this report. The report below provides additional information on confirmed cases, recoveries, and deaths, and gives you the ability to learn more about latest COVID-19 statistics across provinces, states, countries that are closest/most relevant to you. Please keep in mind that this dataset is sourced from a GitHub data repository maintained by Johns Hopkins University and that it refreshes every morning at 5 am central time. Over the next few weeks, we will further iterate to build additional features and visuals in this report, if you have any questions or suggestions please reach out to us at info@kenwayconsulting.com.

 

Kenway Goes the Distance to Provide Value

GUIDING PRINCIPLE

To debate options prior to decisions, and implement the selected option for success. To foster and expand specific technology, methodology and implementation partnerships for the mutual benefit of Kenway, the partners and most importantly, our clients. To remain unencumbered by overbearing alliances with specific technologies, methodologies, and implementation partnerships.

THE SITUATION

Despite numerous challenges and pain points, a telecommunications client continued to utilize an inefficient enterprise reporting system for over seven years. The time required to build reports was overly time consuming, and the outputs were neither accurate nor meaningful. About two years ago, Kenway introduced its client to a reporting and analytics tool, which could potentially resolve their business problems. However, due to difficulties in working with a few key individuals within the software vendor’s sales force, the proof of concept lost momentum forcing the client to continue with their old solution.

STEPS TAKEN

Kenway had a clear understanding of the client’s business problems and believed strongly that a pilot for a new reporting and analytics system was the right approach for their client, so they continued to move it forward. First, Kenway took the initiative to meet with the vendor to communicate their disappointment in how the vendor tried to pressure its client into purchasing licenses without giving them ample time to assess a proof of concept. Second, Kenway leveraged internal reporting and analytics expertise to build a proof of concept independently of the vendor. This proof of concept was used to illustrate how the tool could solve the client’s business problems.

THE OUTCOME

Once the client saw the proof of concept, they decided they were willing to reengage with the software vendor to do a further assessment of the tool. After discussing the situation with Kenway, the vendor agreed to provide the telecommunications company with a new contact point and to let Kenway take the lead in the sales cycle. The client has since agreed to a formal proof of concept project in order to further evaluate the tool and is extremely excited about the prospect of alleviating its current reporting and analytics pain points.

 

Kenway Enabled a Client to Evaluate a New System to Improve Customer Service

 

A Fortune 50 Telecommunications provider needed help in measuring how a major implementation would improve its operations. The communications provider was implementing a new Interactive Voice Response (IVR) system with the goal of enhancing customer service in its call centers through improving routing accuracy. The client had released the new system to a subset of customers and required an analytics solution to investigate how the new system was performing compared to its predecessor.

Successfully executing a transition as large as the implementation of a new IVR presents several major challenges. Without a well thought out and thoroughly vetted set of requirements, the new solution may fail to satisfy all stakeholders, which would defeat the reason for the change. Without clear communication, business rules could be translated incorrectly, negating the value of the requirements gathering effort. Finally, without accurate Information Insight, key performance indicators would be analyzed incorrectly, resulting in incorrect decision-making and adjustments to the IVR.

Recognizing the need to avoid these pitfalls, Kenway worked to carefully plan how to best monitor and evaluate the trial of the new solution early in the project lifecycle. Through a combined effort and bi-directional communication between the client and Kenway, key performance indicators and metrics were defined as requirements were being documented.

Through iterative development, an application was delivered that clearly visualized the deltas in the performance of the two systems. The application also allowed the client to drill down into certain metrics to understand why one system was performing better than the other. For example, views into three key performance indicators that were previously difficult to measure were readily accessible via the new application:

  1. Repeat Caller Rate – This metric reports the number of times a customer makes a call to the client and then calls back within a predefined window of time.
  2. Account Collection Metrics – This indicator displays the collection rate for both the old solution and the new one, enabling a simple side-by-side comparison.
  3. Call Duration – A metric that measures the amount of time a caller spends in each segment of the IVR during a call. This number is a useful statistic for analyzing customer experience.

The final application Kenway created analyzes vast amounts of data with numerous intersecting touch points. Because Kenway and the client were in lockstep on requirements, the end product delivered was directly in line with the client’s needs. The final result was a continued, cyclical approach to update the logic behind the IVR, evaluate the impact of those changes in the application using data driven analysis, and continue to improve the application accordingly.

 

Kenway Ensures the Success of Their Business Partners Despite a Difficult Enviroment

GUIDING PRINCIPLE

To welcome and respect the uniqueness of each individual with whom we engage

To operate, think, demonstrate, speak and lead with integrity and emphasize it through all mediums

To communicate swiftly and effectively through all channels, at all levels, internally and externally, regardless of whether the information is good or bad

To always, under all circumstances and under all economic conditions, do what we believe is “right” for the client, even when what is “right” may directly lead to less business and lower revenues

To include in all engagements, a plan to transition knowledge to clients through training, tools and alternative resources

THE SITUATION

Kenway had been providing Business Intelligence (BI) services to a Financial Institution and its various business units. The BI Lab Team had been a presence at the client for several years and had delivered high impact analysis to their business users. However, several issues had arisen around a new business sponsor, the mistreatment of Kenway employees and the questioning of Kenway’s delivery methodology.

New Business Sponsor:

A client resource was stepping into a newly created role as the business sponsor for the IT group through which Kenway was providing their services. This client demanded that Kenway shift our staffing model to a method that had previously proven incapable of effectively addressing the significant ebbs and flows of project demand for the group. Their demands extended further to require Kenway resources to effectively work as much as the client desired with no potential for relief from additional resources as well as to effectively be “on-call” at any time, regardless of its relation to normal business hours. This directly contradicted both Kenway’s staffing model and our overall viewpoint on how we treat our employees.

Employee Mistreatment:

The aforementioned New Business Sponsor, when challenged on their demands, went on to speak to one of Kenway’s employees in an extremely unprofessional manner. Additionally, another client resource acted unprofessionally in front of both Kenway and client resources, causing direct impacts to Kenway’s level of comfort working within close proximity of the client while onsite.

Questioning Delivery:

As a result of the previously documented situations, the client then began to call into question the delivery method (approach, tools, etc.) that Kenway had successfully been using on this project for well over a year causing unnecessary tension and distractions. Even after Kenway explored utilizing client desired delivery tools and found them to be ineffective, the client insisted that the way Kenway delivered the data was incorrect.

STEPS TAKEN

Kenway assessed the situations individually and holistically. Much debate was had about how to handle this challenging engagement. Throughout this process, Kenway proactively kept all impacted employees informed of the situation, what was being done, what to watch out for, and solicited feedback and concerns from each team member. Ultimately, the decision to discontinue services became the clear best choice. The manner in which to do so was also debated – to terminate services immediately or to ramp down Kenway’s presence over a number of months. In the end, the decision was made to ramp down over a number of months in order to allow for a proper knowledge transfer and to ensure that the business sponsors were fully informed. As a result of this key decision, the following steps were taken:

In regards to the new business sponsor, Kenway worked with its direct clients in the IT organization to minimize any direct involvement with the individual and to instead use the IT clients as a conduit. No changes would be made to the existing staffing model; with the existing staffing model being leveraged to ramp resources down through the completion of the existing contract period. This was communicated objectively and with the background rationale to the new business sponsor as well as the IT clients.

In addition to the actions taken with the new business sponsor, Kenway also worked with the IT clients to relocate to a client site away from the client that had acted unprofessionally. There was further agreement that any engagement with this individual was to be done over a web conference or over the phone.

Finally, Kenway worked with the IT clients to obtain acknowledgement that the delivery model would not change on any project that Kenway performed. Furthermore, both Kenway and the IT clients solicited feedback from their business partners about the delivery methodology to ensure that they were receiving the end products that they desired. The Kenway team also transitioned historical and in-flight projects to the IT clients, allowing them to modify or complete them in the manner they deemed fit.

THE OUTCOME

Despite the difficult client situation, Kenway continued to provide optimal consulting services. Rather than taking the easy road and simply disengaging immediately with the client, Kenway opted to take the more challenging, but right road. Kenway did not want the actions of a few individuals to negatively impact the majority clients who valued our services and approach. The Kenway team diligently documented and transitioned its work in order to best position the group to succeed moving forward. In the end, Kenway resources were able to maintain their focus and were appropriately rewarded with the praise of their business partners for a job well done on the respective projects that Kenway completed.