3 STAGES OF BECOMING A DATA-DRIVEN ORGANIZATIONtheissaac.com/wp-content/uploads/2019/02/TargitJourney.pdf · 3 STAGES OF BECOMING A DATA-DRIVEN ORGANIZATION 4. BI IS A JOURNEY KNOW

3 STAGES OF

BECOMING A

DATA-DRIVEN

ORGANIZATION

TABLE OF

Contents

BEFORE ANALYTICS COME PRE-ANALYTICS . . . . . . . . . . . . . . . . . . . . 1

PRE-ANALYTICS: THE SEEDS OF BI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

BI IS A JOURNEY KNOW WHERE TO START . . . . . . . . . . . . . . . . . . . . . 5

PLAN THE BETTERBI STRATEGY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

BASIC BI & ANALYTICS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

REAL-TIME DATA DISCOVERY & ACTION . . . . . . . . . . . . . . . . . . . . . . . . 13

COMPETETIVE ANALYTICS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

MISTAKING THE MAP FOR THE TERRITORY –

AND INERTIA FOR PROGRESS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

3 STAGES OF BECOMING A DATA-DRIVEN ORGANIZATION i

OVER REVOLUTION

THINK EVOLUTION

BEFORE ANALYTICS

COME PRE-ANALYTICS

Static Excel spreadsheets pulled from a complicated ERP system aren’t business intelligence. Pulling live data from multiple sources and building dynamic analyses is. And that’s just the beginning. But to get from Point A to Point B isn’t something that happens overnight.

Even though some companies do make progress from what to why faster than others, practically all organizations pass the same milestones as they develop into data-driven powerhouses. At TARGIT, we see this as a three-stage journey that ends with a strategic organization that has empowered all employees to make informed decisions faster and more confidently than ever before.

3 STAGES OF BECOMING A DATA-DRIVEN ORGANIZATION 1

In this eBook we’ll introduce you to the preparation that must be taken to lay the groundwork even before BI can be implemented, as well as how to make the natural leap from one BI stage to the next. The journey progresses along in accordance with the increasing amount and complexity of data able to be analyzed and the tools that allow you to do so. Other signposts indicate growing depth and breadth of analytics throughout the organization, more frequent discovery cycles, and improved analytical skill sets.

Ultimately, however, progression along the path toward the data-driven business is also one of cultural transformation. The organization’s decision-making processes become more transparent, fact-based, and widely adopted.

Data-informed analytics help shape bigger strategic decisions as well as optimize an increasing amount of operational processes. Business intelligence becomes a tool used by employees across the company, as both management and staff are increasingly ready to embrace the changes that data-driven insight enables in order to gain competitive advantage.

Putting BI and analytics in the hands of every decision maker aligns company goals and ensures everyone is on the same page with the most updated information in real-time. There are no “different versions of the truth” floating around in disparate spreadsheets, and every user has a consistent experience across platforms.


PRE-ANALYTICS:

THE SEEDS OF BI

The startling fact is, there are more businesses who don’t yet use a comprehensive business intelligence solution than those who do. Or worse, those who think they’re employing business intelligence, but are eons away from access to the insights they need to make truly data-driven strategies. While mom and pops and other small businesses make up the obvious majority in terms of sheer numbers, there are still plenty of multi-million dollar companies here as well.

These companies are using the same sorts of management information systems today as they did 20 years ago: standard reports produced via their ERP systems and static analyses created in Excel. We consider this a pre-analytic stage because these companies have not yet entered into the BI journey.

There’s a plethora of problems with this outdated system for measuring and monitoring KPIs and general business health. Users are likely consolidating reports from different data sources. This is time-consuming, tedious, and open to human error. Usually it can only be done by the IT department so those who need the information can’t pull it or adjust it themselves.


As such, it takes days to pull a report. That means KPIs are only being monitored monthly or weekly at best. At a minimum, they should be measured daily. Ideally, they’re monitored 24 hours a day, seven days a week. If it takes days to run a single report, it’s simply not possible to make strategic business decisions. In other words, decisions here are being made reactively instead of proactively.

Not to mention, this results in cloudy insight into data and different versions of the truth floating around as data is manipulated in individual static reports and spreadsheets. This stage is commonly referred to as “Excel Hell.” Don’t get us wrong; we love Excel, but a pivot table is not business intelligence. And when it comes to cross-company deployment of insights, this is the platform where we most often see different versions and outdated data circulating throughout companies.

Another pre-analytics tool disguised as BI we commonly see in practice is the single-user data visualization platform. While these tools give users the ability to create a data visualization on the fly, they’re truly only one step removed from Excel. These data visualization engines simply repurpose those Excel numbers into a more digestible image. Without the ability to get to the why behind the what, this is no better for a company striving to be data-driven than a simple spreadsheet.

A PIVOT TABLE IS NOTBUSINESS INTELLIGENCE


BI IS A JOURNEY

KNOW WHERE TO START

When first starting out with any business intelligence project, companies should take BI baby steps. It would be equally foolhardy to walk out one’s front door and run a marathon without proper training and preparation as it would to attempt to implement a full-scale BI solution without first strategically planning for a successful, long-term project.

Furthermore, you can’t buy your way up the BI maturity model. That’s like buying an expensive violin and expecting to instantly be a virtuoso. The organization must change over time.

Think big but start small. Map out where in the business BI can make the most significant impact both now and over time so there is a clear picture of what will be accomplished right away and what will be added on throughout the scoping process.

Outline the goals, plans, and means to increase investment in the specific areas that will need a higher level of maturity. Use a strong maturity model to define what success looks like.

TH I N K

B I G

S TA R T S M A L L


MAP THE METRICS THAT MATTER

The first step in documenting this model is to establish the metrics that matter most. This prevents information overload and ensures employees are focused on the most important KPIs.

This is a critical step, and one where companies most often struggle. You can use the guide “The Metrics That Matter” to help you determine the right KPIs for your company.

DOWNLOAD THE GUIDE

CONSIDER BI USER PERSONAS

Once it’s been determined what should be analyzed, one must consider who should be analyzing and what their roles and responsibilities are throughout the company. Employees’ use of BI varies widely across organizations and functions. Some need advanced analytical functionality, while others prefer high-level dashboards tracking a few key KPIs.

BI isn’t one-size-fits all. Each analysis, dashboard, and report must answer business questions in a way that makes most sense to those who need to know the answers, otherwise it’s unlikely employees will bother to make it part of their daily work life. We define these users as four distinct BI User Personas that you must always consider when working with any business intelligence tool: Information Consumers, Business Users, Business Analysts, and Information Designers.

Read about who theses Personas might be at your company, how they use BI, and recommendations for each in the guide, “How to Ensure the Highest User Adoption Rates for your BI Project.”

LEARN ABOUT PERSONAS


PLAN THE BETTER

BI STRATEGY

Next, it’s time to define what we at TARGIT call the Action Loop – otherwise known as the OODA Loop -- in which decision-makers travel through the cycle of observation, orientation, decision, and action.

A decision-making process that is firmly modeled on the Action Loop is the first step in an efficient, data-driven organization. Every decision-maker in the organization should have a comprehensive understanding of what information is needed for each stage of the Action loop that relates to his or her responsibilities, ensuring that the business intelligence project is best designed to answer the most important business questions.

Every decision within a company should travel through the four stages of Loop. At any given time, a single company will have dozens, if not hundreds, of loops spinning at the same time. Natural side effects of the Action Loops are increased organizational learning and continuous process improvement.

You can read about defining your company’s Action Loops and their role in creating an efficient, data-driven organization in the guide, “the Better BI Strategy.”

START STRATEGIZING


ONCE YOUR COMPANY IS READY TO MOVE FROM THE PRE-ANALYTICS STAGE AND A BUSINESS INTELLIGENCE

PROJECT PLAN HAS BEEN SET IN PLACE, YOU’RE READY TO GET YOUR FEET WET WITH BI.

These are the three stages of your BI journey that will guide you towards the ultimate result of an agile, optimized, data-driven organization.


BASIC BI &

ANALYTICS


This first stage is the operational level of BI. Basic reports and analytics are pulled easily. Management has committed to a unified version of the truth based on approved data sources, and starts to dig into internal data to reveal new kinds of actionable insight.

At the beginning of this BI stage, analytics is often the tool of a few super users in the IT or Finance departments. As the organization becomes more mature in BI and analytics, standard reports give way to specialized reports. A continuous cycle of better insight leading to better questions and answers is set in motion.

Typically, those utilizing the solution in the beginning BI stage are IT, Finance, and Sales. But information is easy to disseminate throughout the organization with dynamic storyboards and mobility features so information remains consistent and up-to-date.

By the end of this stage, an increasing number of people in an increasing number of departments are embracing BI and analytics. This is still BI at an operational level, but fact-based processes are on the rise. The Action Loop begins to spin.

11

ACTIVITIES & OUTPUT: advanced reporting, query-based analyses, company/department dashboards, storyboards

PRODUCERS: IT, finance, and sales

USERS: finance, functional managers

TOOLS: dedicated BI and analytics tools

OUTCOMES: tactical decision making based on produced information and transparent KPI reporting

DATA: pre-built cubes using internal data

3 STAGES OF BECOMING A DATA-DRIVEN ORGANIZATION

REAL-TIME DATA

DISCOVERY & ACTION


As the organization grows in analytics maturity, it begins to expand its ambitions to ever-broader data sets. The volume of structured and unstructured data available for analytical use increases – often exponentially – to include sources from beyond the company ERP system.

As data volume, variety, and velocity increase so does the organization’s ability to make use of it. Analytics resources and skills are no longer limited to a few departments, and everyone from Sales to Marketing and Supply Chain is leveraging the benefits.

Decision making is spread throughout the company because business intelligence is arming users with the information they need to make decisions and take action with confidence. User adoption increases with features like embedded BI, which places business intelligence directly in the applications and portals that employees work in most such as SharePoint, ERP, or CRM solutions. And mobile BI makes it possible for users to access the information they need from any device, no matter where they are.

Thanks to advances in data governance capabilities—which ensures only the right people see the right data at the right time—business partners and other external collaborators can also access relevant information to make better decisions throughout the entire value chain. The more people that are brought into the Action Loop, the faster it spins. This is where the true value of self-service BI lies.

Increasingly, analytics is used not only reactively – to look back on past performance and inform corrective action – but proactively to make real-time operational adjustments. Automated notifications based on multi-dimensional analyses keep people informed of key developments without keeping them glued to the screen. Predictive analytics learns from the past to provide educated guesses about probable futures.

Real-time connections to data sources mean data is always up to date, allowing for faster reactions when decisions need to be made. These real-time analytics capabilities are critical, especially in business areas such as operations, where business situations can turn on a dime.


Users at this stage have begun to take advantage of some of the more powerful BI tools. Data discovery with data mashup provide instant answers to variables inside and outside of the data warehouse. With user-friendly ad-hoc analytics tools permeating the market, big data discovery is no longer just a skillset of the controller or BI super users.

As the value of a data-driven company culture becomes increasingly clear, decentralized users throughout the organization begin embracing BI tools to better understand their business areas. User-friendly dashboards help make creating and sharing comprehensive reports and analyses easy.

As user adoption spreads, the organization is increasingly aligned on goals and current statuses. Fragmented and outdated data sets become a thing of the past. The Action Loop is integrated throughout the company, empowering decision-makers to move from observation to action even faster.

14

ACTIVITIES: data discovery, predictive analytics, modeling, ad-hoc analyses, real-time analytics, data visualization

OUTPUTS: new knowledge based on multi-dimensional analyses; individualized dashboards, alarms; stationary and mobile platforms

PRODUCERS: users

USERS: director-level management, sales, marketing, supply chain

TOOLS: data discovery tools, mobility, individual/project dashboards

OUTCOMES: proactive decisions based on analytical insights that lead to strategic/tactical business improvements

DATA: internal/structured sources and external/unstructured sources


COMPETITIVE

ANALYTICS


Although we propose this stage as the last of three for the data-driven striving organization, there’s nothing final about it. The leading edge of analytics is constantly moving ahead and changing whatever game it’s playing in.

Companies operating at this level of maturity use data disruptively. They employ the most highly skilled analytics resources they can find – and they often can’t find enough of them.

This is when ad-hoc analytics with external data sources really come into play. In other words, this is when companies really start cooking with gas with big data. Today, BI users have tools at their fingertips to tap into the power of external data sources such as Microsoft CSV, Azure Data Market, Directory, Excel, OData, ODBC, and Quandl to analyze data against their own internal company data. With so much data available, it won’t be long before a company’s external data far exceeds their internal data.

Consider the doors of insight blown wide open with these capabilities. Companies can now pull in information such as weather data to see how the weather has impacted seasonal revenue. Or map demographics to plan the most ideal placement for the location of a new retail chain location. Market price fluctuations can be integrated over internal data to determine if pricing is competitive. Savvy companies can even track raw materials pricing to decide which products to launch now and how to best engineer for the future.

The level of dynamic strategy increases along with the growing number of data sources and data size. With big data continuously expanding, companies are looking for ways to harness the tsunami of data to glean insight from what is relevant to them. Companies who embrace advanced analytics use data insights both strategically and operationally in an ongoing spiral toward competitive advantage.


In this stage of BI, companies rely on the tools that ensure proper data governance along with the ability to experiment with data before it’s brought into the data warehouse. This ensures new innovation and experimentation can be folded into the traditional business intelligence strategy. This new BI strategy is designed with flexibility and mass distribution in mind.

The ability to play with big data sets and analyze them on top of what’s already in the data warehouse encourages employees to think strategically without the need to pull in IT. At this level, there is a dynamic integration of BI. Organizations are contending against the competition based on their analytics. Internal and external data sources are at play. This is a true data-driven culture.

ACTIVITIES: analytics, data mining, proactive sensing, experimentation

OUTPUTS: insight and foresight

PRODUCERS: business analysts and users

USERS: all BI Personas

TOOLS: dedicated BI and analytics tools capable of working with massive data volumes

OUTCOMES: strategic innovations of products, services and business processes

DATA: large or very large quantities of structured as well as unstructured internal and external data


MISTAKING THE MAP

FOR THE TERRITORY –

and inert ia for progress

This three-stage approach is a model, not an exact representation of reality. The stages are clearly not pure states.

Nonetheless, organizations can use this roadmap to their advantage. Based on decades of experience turning customers into data-driven organizations, we know that companies don’t progress from stage one to three without spending time in between. Most importantly, a company should evolve along an axis that gives them better customer insight and market agility.

It’s critical to strive for incremental, evolutionary improvements that are achievable, rather than revolutionary leaps that promise fantastic results but also require significant shifts in corporate culture.

Another lesson learned is that moving from one stage to another requires organizations to change. And as we all know, change isn’t easy. We see too many companies evolve from level one to level two only to see inertia regain control. Instead of continuing to push for greater analytics power throughout the organization, they level off in what becomes a new comfort zone.

Adaptation is the key to survival in every industry. The courage to act is also the courage to change old habits – and keep progressing on the path toward the data-driven business.


WONDERING WHERE YOUR COMPANY LANDS ON THE JOURNEY TO BEING TRULY DATA-DRIVEN?

GET A FREE BI MATURITY ASSESSMENT FROM TARGIT TODAY.

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3 STAGES OF BECOMING A DATA-DRIVEN ORGANIZATIONtheissaac.com/wp-content/uploads/2019/02/TargitJourney.pdf · 3 STAGES OF BECOMING A DATA-DRIVEN ORGANIZATION 4. BI IS A JOURNEY KNOW