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Data-driven

How to Make Your Company Data-Driven

[et_pb_section admin_label=”section”] [et_pb_row admin_label=”row”] [et_pb_column type=”4_4″][et_pb_text admin_label=”Text”]With the development and growth of big data technology, the ease and cost-effectiveness through which companies can acquire and store data is high. With this ease comes the opportunity for companies to become more data-driven in their decision making. Data also helps companies compete on analytics, whereby the insight they glean enables them to differentiate themselves from competition and battle more effectively in the marketplace. How can companies be more data-driven? To answer this is to understand how analytics are applied in an organization. At a company macro level, internal analytics are used to measure effectiveness of strategies and progress towards business goals. Business goals and strategies must be decomposed into an analytics strategy that defines an agreed upon approach for how analytics programs are defined and executed. To be more data-driven companies need to get value from their analytic programs. Cohere Insights has a process and framework for how companies get value from analytics programs to achieve the highest objectives of the business. The process and framework is represented through the Analytics Value Pyramid, which breaks down the value components into segments, where each segment is fulfilled towards the highest value possible. Analytics-Value-Pyramid-1.png

How the Analytics Value Pyramid Enables High-Value Analytics Programs

The highest value to achieve with analytics is towards business goals. Business goals are broad objectives that companies set out to accomplish each year that enable them to thrive and grow. These typically include revenue growth, cost stabilization, customer acquisition and retention, and productivity and efficiency increases. Organizations decompose goals into business strategies for how they will accomplish them. Strategies vary by industry, company size, product and market.

Analytics Strategy

Without analytics, a company cannot measure the effectiveness of its business strategy. To achieve greatest long-term value, it is well worth the time and effort to define an analytics strategy and decompose it into a set of analytics applications built from a user’s view with the data a company has or can acquire. Launching from business strategy into the details of data and technology directly is where many companies fail in their efforts to become data driven. What should you consider in your Analytics Strategy?
  • KPI’s you will measure, lagging and leading indicators.
  • Decision making analysis: this is closely tied to KPI’s and is the process of taking a business situation and converting it into an analytical use case that identifies beyond the KPI’s what action you will take to correct the business and bring the KPI’s into a range of acceptance.
  • Who-Why-What-How: every analytics strategy should include background on who will be using the analysis, why they are analyzing the data, what data is available or can be acquired, and a high level of how they will use technology and engineering to analyze.
  • Program strategy: this is the strategy for the technical implementation that will deliver the solution. It decomposes the analytics strategy into one or more detailed technical programs/projects delivered by project teams.

Analytics Applications – right data, right form, right user, right time

From Analytics Strategy, the next segment within the Analytics Value Pyramid is the analytics applications themselves. One of the most important considerations for analytics applications is that the right data be presented in the right form to the right user at the right time. Analytics applications should be built from the user’s point of view based on the way they understand and consume business information. Analytical applications include the obvious instruments, like reports, graphs, dashboards, scorecards, all of which should have drill-through capabilities for investigative analysis. Other types of analytical applications include exception alerting and mobility, and user data models, which are models of integrated data from various systems that provide a business user’s view (as opposed to a database physical schema view).

Data and Platform Technology

Data and platform technology are the building blocks for analytical applications and are at the base of the Analytics Value Pyramid. They provide business value through each of the upper segments. Data without a user application and an analytics strategy doesn’t by itself provide business value towards achieving business goals. This base segment layer includes:
  • Original source data.
  • Integrated data from multiple sources residing in a data warehouse or data lake
  • Platform and visualization technology that stores integrated data, and queries and visualizes it for the user.
  • Data feeds and data pipelines – engineered scripts and feeds that transform and pipe data into a data repository for the purposes of analysis through applications.

Continuous Feedback Loop and Information Flow

Rare is the company that takes an exclusive top down approach to analytics. In my experience the practice is a combination of top-down, bottoms-up, meet-in-the-middle, which works because of the top-down component. Companies that are operating purely from a bottoms-up approach expose themselves to the risk of analytics project failure and often produce applications that do not offer enough business value and have less adoption.

Feedback Loops

Feedback is a critical component of high-value analytical programs and is bi-directional within the Analytics Value Pyramid. Business value is obtained when feedback from the top-down is incorporated into applications, because it enables the applications to continuously evolve with the business itself. Likewise, feedback up the value pyramid addresses issues with data, like integration and quality issues, and issues with the platform technology itself or how it’s implemented. Companies wanting maximum value must design and maintain feedback loops into their process for building and implementing analytics.

Summary

There are many variables that affect a company’s path towards business value from analytics and data-driven decision making. It is not necessarily company size alone, it includes process and program maturity and scope of how it wants to use analytics. Purpose specific and departmental analytics programs have shorter speed-to-value and delivery windows than sweeping programs that start from the top of the organization down. Regardless of where a company lies on the data-driven and analytics readiness spectrum, the Analytics Value Pyramid provide a framework for how to get the most value in the shortest time possible. Andrea Amaraggi is Founder and Principal of Cohere Insights. She helps companies leverage technology and engineering to build business focused analytics applications that drive revenue, reduce costs, achieve operational excellence, and create customer successes. Contact her at andrea@cohereinsights.com.[/et_pb_text][/et_pb_column] [/et_pb_row] [/et_pb_section]
analytics

From Business Goals to Outcomes – How to Frame your Approach to Business Analytics

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To sustain and grow, companies set business goals they want to accomplish each year. A company, depending on its vertical, its size, its operating market, or other business variable, will pursue set of goals that is unique to that organization. Revenue and profit are the most ubiquitous, but others include cost stabilization and control, customer and employee retention, or productivity and efficiency maximization, to name a few.

Beyond business goals, companies develop business strategies for how to accomplish them.

Goal: Revenue Growth ⇒ Market Penetration & Development

In the case of revenue growth, a company can pursue a strategy like market penetration or market development, where they sell their existing products in their current market or adjacent markets. A great example of market development is Ike’s Sandwich Shops. The original Ike’s opened in San Francisco in 2007, and after finding a successful formula, opened additional restaurants in the SF bay area before expanding to markets outside the bay area and outside of California.

Goal: Revenue Growth ⇒ Channel & Product Development

A company can also pursue a business strategy of channel or product development to achieve its goals, where they sell existing products in new channels or create new products for existing or new customers.

An example of this type of business strategy, and one of my favorite female-led success stories is that of Pepsico and it’s recently retired CEO, Indra Nooyi. She was CEO for 12 years before retiring in 2018. In her tenure, Pepsico’s revenues increased by 80% as it introduced new products, including healthier foods targeted to female buyers, to counter declining interest in sugary beverages.

The Role of Analytics

What do business goals and business strategy have to do with analytics? Analytics is the process for measuring how well business strategies are succeeding and driving outcomes. Analytics also helps identify business opportunities for achieving goals that the company hadn’t previously strategized about.

In my last blog post, I provided a great example of exactly this for the restaurant chain industry, where an analysis of the breakdown of sales within and across restaurants exposed opportunities to offer dining incentives to existing customers and to eliminate unprofitable shifts like mid-day lunch, where there wasn’t enough business to support them, thus reducing restaurant operating costs.

Cycle of Analytics (1)

As companies define business goals and strategies, they implement a series of tactical organizational and business process changes to accommodate the strategies. These could be organizational or hierarchical changes, new product launches, new or updated internal systems (ERP, Finance, HR, Go-to-Market, CRM, for example). Each of these changes creates data within its proprietary application. The data, when integrated across systems, provides the foundation for analytics and is used to measure effectiveness of tactics and strategy, in order to drive business outcomes.

Based on analysis of the measures (data aggregation) companies can make changes to their tactical implementation or to their business strategy, or both, to get the business outcomes they desire.

Analytics Strategy

Having a well defined analytics strategy enables you to reap the greatest rewards of using analytics to measure effectiveness of strategy and tactics. An analytics strategy ensures that you are measuring the right activities for the right users using the right technology for your organization. With an analytics strategy in place, that defines the who, why, what, and how of your analytics program, as well as prioritization of delivery and the technical program that delivers it, you will have a numbers-driven approach towards achieving goals.

In my next blog post, I’ll talk about the role of data and data platform technology (collection, integration, visualization) on the actualization of business strategy and the achievement of business goals using Cohere Insights’ Analytics Value Pyramid. The Analytics Value Pyramid hierarchically categorizes the process of achieving business goals through data, where there is a continuous feedback and information flow up and down the hierarchy (data to goal and vice versa).

The pyramid is a great instrument for demonstrating the value relationship between data, technology, analytic apps, and what the business wants to accomplish. When articulated across teams, it creates buy-in on approach and facilitates delivery of successful analytics applications.

Andrea Amaraggi is Founder and Principal of Cohere Insights. She helps companies leverage technology and engineering to build business focused analytics applications that drive revenue, reduce costs, achieve operational excellence, and create customer successes. Contact her at andrea@cohereinsights.com.

analytics

How Analytic Scorecards identify new revenue growth and cost savings opportunities

[et_pb_section admin_label=”section”] [et_pb_row admin_label=”row”] [et_pb_column type=”4_4″][et_pb_text admin_label=”Text”]analytics-business-chart-920116.jpg An analytics scorecard is one of the best ways business leaders can use data to measure business performance, predict future outcomes, and identify revenue growth and cost savings opportunities. It’s effective because it’s numbers driven, fact-based, and uses a straightforward format that makes the information easy to consume and digest. When defined well, it creates actionable insight.

What a scorecard is

A scorecard is an analytical instrument containing a handful of important KPI’s, which are metrics aggregated from data the business has sourced from its internal systems, or data it can acquire from outside the company. The format for scorecards varies from industry to industry, business to business, and even from department to department, depending on who the scorecard user is and how they will use it. A C-level executive will require a set of KPI’s that covers all business lines of the company across operating regions/countries within his or her purview. A department lead, like a retail buyer for example, will require a different set of KPI’s, such as those on a vendor scorecard, that enable the buyer to evaluate vendors and negotiate favorable buying terms.

KPI’s to include

In my experience, there are two main types of indicators to use in a scorecard: 1) lagging indicators, and 2) leading indicators.

charts   Lagging Indicators

Lagging indicators are the top level metrics that provide the initial view of what’s happening in the business. A lagging indicator measures numerical output of business activities. The most common lagging indicators are sales, cost, or profit — measured in units, whole currency, or as a percentage to total. A lagging indicator could also be a count, average, minimum, or maximum, based on factual data. This is not a definitive list, there are others, depending on the business.

discount  Leading Indicators

While lagging metrics measure output, leading indicators are aggregations of activities that drive the lagging metric. A lagging metric has one or more metrics that lead into it. For retailers for example, a lagging metric is sales, and some of the leading metrics that drive it are customer or transaction count, average basket size, and basket assortment.

Deeper dive analysis and actionable insights

Any of the above leading indicators can drive revenue, and the scorecard should support a deeper dive analysis, so that the user can drill down and identify opportunities or estimate root cause of a problem, and take action where necessary. Last week I spoke with Teresa Curtis, retail expert, and former Senior Manager for Gap, Inc, to talk about how she uses data in retail. Her response: “I would look at the numbers to tell a story of what happened in the past, and then use that analysis to modify the story for the future.” Here’s a great example of exactly this, from the chain restaurant industry. pexels-photo-262978.jpeg Using sales data on a scorecard, we can drill down to analyze guest preferences and habits. Through analysis, we can identify that in some regions, Sunday family brunch is more popular than in other regions. We can also identify where business lunches occur with greater or less frequency than in other regions. Analytical findings:
  1. In the locations where family brunch is more popular, we can drive sales by advertising with features like a kids menu, holidays celebrations, or special birthday events.
  2. In the locations where there is low cover count during business lunch, we can reduce staff or close during lunch hours.
  3. In the locations where there is high cover count during business lunch, we can entice guests with offerings to come in for dinner or visit on the weekend, to extend the momentum of the weekday business lunch.
These findings are actionable, and the impact of the action can be measured. That’s the virtual cycle of analytics. Let’s break this down by scorecard component: Scorecard-process-2.png
    • Lagging indicators: Sales by restaurant, region
    • Leading indicators: Customer count by service type, time of day, day of week
    • Analysis: Identify target sales growth or cost reduction opportunities by restaurant, region
    • Actions: Targeted advertising based on sales growth opportunities; staff and/or operating hours reduction
    • Outcome: Increased sales in target growth areas, reduced operating costs

How to create a great scorecard

Untitled-drawing-1.pngAs with any analytics endeavor, the best place to start is with a high level strategy for the who (users), why (why are we analyzing, what do we expect to gain), what (what data do we have available), and how (high level technical componentry for how it all comes together). From these four questions, the next step is to decompose it all into a program that includes detailed requirements and technical delivery of the solution. Getting agreement and alignment from stakeholders on KPI’s and how they are calculated is of utmost importance, and from experience, can be the most challenging part of the process. If KPI’s are not meaningful to the user audience or if the audience doesn’t trust the calculations behind the metrics, the scorecard won’t get used, and users instead will develop manual workarounds in Excel spreadsheets, which is not a sound, sustainable business practice.

Final thoughts

There is tendency to have analytics and data technology and engineering capabilities be the central focus for how a scorecard comes together, but a business scorecard is a business instrument built for the business. The technology and engineering provide the means to create it. To ensure success, it’s best to have a clear view of what the scorecard is supposed to be, why you need it, and how you will use it from a business perspective, and then dispatch the right engineering teams and the technology to build it. In the end, it should be scalable, sustainable, and supportable, and of course accurate and easy-to-use. Andrea Amaraggi is Founder and Principal of Cohere Insights. She helps companies leverage technology and engineering to build business focused analytics applications that drive revenue, reduce costs, achieve operational excellence, and create customer successes. Contact her at andrea@cohereinsights.com[/et_pb_text][/et_pb_column] [/et_pb_row] [/et_pb_section]
Budgeting

Top 3 Analytics Budgeting Misses

Analytics Budget Misses1

Are you budgeting for analytics programs in 2019? Here are the top 3 things managers frequently miss or underestimate in their analytics budgets:

  1. Stakeholder Needs Assessment. Investment in thoroughly identifying stakeholder needs and wants, both broadly at inception of the program, and narrowly during requirements phase. Stakeholders are not always sure of what they need, so it’s important to have an experienced guiding hand to draw out the specificities of how they will use analytics. User needs can be diverse, even for users within the same function, so it’s important to not short-change the process to collectively identify needs across the functional area that you are delivering on, whether it’s sales/revenue, marketing, customer engagement, HR, finance, or other area of the business.
  2. Technology Costs. Underestimating the costs of technical delivery, including data transformation, data infrastructure, and data delivery. This is driven from #1. Stakeholder needs will drive the technological methods for how you will collect and transform data insight to satisfy stakeholder analytics needs. There is also a dependency on vendors that will provide the technology, or on the internal teams that will build platforms if you use a build vs. buy approach. It’s important to ensure you have commitment from vendors on pricing structure that will satisfy productionalized systems that will be supported and maintained post-deployment.
  3. Realistic program delivery resources. This is driven from #1 and #2. Stakeholder needs and technical delivery drive what human resources you require to build, deploy, and support analytics programs. This includes on and offshore resources, full-timers, consultants and contractors for positions in program and project management, engineering, application development, business and systems analysis, testing, training and documentation, as well as liaison roles like a relationship manager, for ongoing user needs analysis and adoption support.

Contingency. Every program and project contains an element of uncertainty, as not all aspects of the program can be estimated and planned at the outset. In other words, stuff might happen that negatively impacts the program, even at the hands of an experienced team, so adding contingencies into your budget provides a buffer for unintended or unexpected obstacles.

Need help? Cohere Insights is a consultancy that helps organizations with #1 and #3. We advise on #2. We provide analytics advisory and consulting services and end-to-end technical implementation of analytics and business intelligence applications.

www.cohereinsights.com
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analytics strategy

4 Questions to Ask to Define your Analytics Strategy

question-2519654_1280According to IDC Research, worldwide spending for big data and analytics will hit $187 billion in 2019. And from IDG, within the CIO Tech Poll, data and analytics are expected to have the greatest organizational impact [out of all applications] over the next three to five years.

Companies are investing in analytics, and to get the most out of their investments, they use strategy to pull it together.

What an Analytics strategy is and how to define one

analytics-charts-computer-669612 An analytics strategy identifies a set of business, organizational, or functional goals for what you want to accomplish with analytics, and follows with aligning stakeholders to those goals. Alignment means buy-in and agreement, and sets expectations around what each stakeholder’s role is for ensuring success of the program.

Beyond goal setting and stakeholder alignment, the next step in strategy is to define how you will decompose the goals into a set of implementable metrics and how you will deliver these metrics within a technical program across a timeline. You can fill in this step by answering four questions on the whowhywhat, and how of your program. The answers will define the backbone of your overall strategy, and from strategy, you define an integrated program and the projects that will deliver it.

The Who

The who of your strategy defines the intended audience for the analytics program. The who is any unit, function, or group of users that require insight for the data you have or can acquire.

An example of a function are teams within HR, like recruiting, compensation, or benefits. If increasing productivity is a main strategic goal of your organization and if analytics will support that strategic goal, this would translate as increasing productivity of new hires by ensuring they have access to company knowledge and tools to do their jobs. This objective requires an analysis of hiring cycles to ensure training and onboarding programs align to recruiting and hire efforts. The who is this case are HR analysts, recruiting managers, and training program managers, as well as IT support and helpdesk (for technology onboarding), and any other teams that have a hand in the overall onboarding process.

Roles. Within each of the functions you define as the who are user roles, such as analyst, manager, director. Defining user roles helps clarify the level of data detail the user will consume, which is useful when you get to the what and how components of the strategy, i.e., what data you will analyze and how it will be collected, aggregated, and visualized for the user.

If you are defining an enterprise or sub-enterprise strategy, the who may be multiple groups of users across functions, or a team of executives that oversee multiple functions. An example of this is an executive scorecard, which has a target audience of C-level executives, whose purview spans multiple functions within an organization.

Defining the who is also input for the way in which you will deploy the analytical solution and how you will drive and measure adoption of it. Power users, analysts, and more technically trained users need less application-level abstraction and less adoption efforts. More business focused users may need more training, more application abstraction, and more pushes toward adoption efforts, all of which you should consider in the overall strategy, because adoption scale can lengthen the development cycle.

The Why

If the who defines organizational function and/or user groups, the why defines why they need analytics in the context of their user role. This step is closely aligned to decomposing the program goals that I wrote about in the earlier part of this article.

Using the earlier example of HR analytics, if an organization’s goal is to increase productivity, this applies to new hires to ensure they are as productive as possible in their new jobs. Aggregating data around projected recruiting and hiring cycles (what time of year, how many new hires, and in which departmental area) will drive training programs and onboarding efforts. Aligning these two HR functions helps ensure expedient onboarding and training, and the analytics support this alignment.

At a strategic level, it is not necessary to dive into specifics of detailed metric definitions, you will perform that process during requirements definition. The goal at the strategic level is to provide over-arching definition that you will decompose at the program and project levels.

In my experience, the why is the hardest of the four questions to answer, because it requires a large amount of discussion, negotiation, and agreement by stakeholders about what they are measuring and how it drives the strategic goals of the organization. Because defining the why drives the what and how, which are the two largest components of the strategy (and most expensive to develop), you should not undercut the investment of time to get this right.

The What

The what answers questions about the data, including:

  • What data you will use in the analysis
  • Where you will source it from
  • How you will integrate it
  • What level of granularity you need
  • What volume of history you need
  • What aggregations and metric definitions you need

The what is inextricably linked to the why. What data you are analyzing and why drives how you will transform the data and whether you will apply scientific models, like machine learning or predictive models.

The what is also a perfect place to overlay data strategy to provide a superset of governing principles around the data, such as security requirements, access requirements, and history requirements.

The How

The how is the technological meat of the program and defines the technical methodology that will transform data into insight consumed by end users. The how includes technological platforms, application and data integration, data transformation, and query and visualization layers. The how can also specify whether you intend to deploy a self-service user model and through what means users will access the analytics, such as through web, mobile, email, message, chatbot, embedded form, or through some other means.

The how is also the place where you can introduce specific technology approaches, such as database and computing platforms, query and visualization tools, and whether you are considering building vs. buying technology.

At a strategic level, it is not necessary to identify all the technological and process details, the intent is to demonstrate a direction of thinking or organizational thought process around what the future of your analytics technology platform will be and how you will deliver it and support it for users. This can also be driven by over-arching technology principles your organization practices, such as building vs buying, or only using cloud computing vs. onsite.

Prioritization

Throughout the process of defining the who, why, what, and how, you should use a method for prioritizing the answers to these questions, because you won’t be able deliver everything out of the gate. Prioritization enables you to define short, mid, and long-term objectives and deliver them in a phased approach.

Integrated Program

After defining the backbone of the strategy that answers the who, why, what, and how, the next step on your analytics journey is to define an integrated technical program for how you will deliver on all the moving parts. The program provides a strategic view for end-to-end delivery and decomposes the view into one or more phased delivery projects. Each delivery project has a discrete output, where the output is a unique component within the overall program.

Delivery projects organized using an agile methodology enable you to practice iterative development and execution, which means deliverables can be re-prioritized based on fluctuating environmental circumstances, and can be decomposed into development cycles of 2 to 4 weeks.

Where to go from here

With a strategy defined and a program outlined, you are ready to start using the processes, people, and technologies that will deliver the goals of the program over your target timeframe. Program delivery requires a range of people resources, including program manager(s), project managers, business and systems analysts, solution architects, data engineers, application developers, visualization experts, testers, trainers, and documentation specialists.  You can use in-house resources or you can work with a consulting company for specialized resources to augment your current staffing for short-term purpose.

However you staff your program delivery, your strategy becomes your guiding document throughout the process, and provides the roadmap for your analytics applications that deliver business value, that are sustainable and supportable, and that your users want and like to use.