Updated: Jan 26
It is no secret that data-driven organizations are superior at innovation, competitiveness, and CX. However, in a rush to capitalize on this growing trend, I’ve seen many clients fall painfully short.
The cautionary parable usually goes something like this:
Once upon a time in a far-off kingdom, the ruler was a wise and just king. However, there was one problem: the kingdom was not prospering and was under constant threat from rivals.
The king would make decisions based on gut feelings and advice from members of his court, but they were often wrong. The kingdom was losing money, resources, and citizens left and right.
One day, a wise old wizard came to the king with a proposal. He said, "Your majesty, if you invest in Data Analytics, your kingdom will be prosperous and your people will be happy." The king was skeptical, but he was desperate for a solution. He asked the wizard, "What do you mean by Data Analytics?"
The wizard explained, "It means using data to inform all of your decisions, from where to build new roads to what crops to plant. It means using data to optimize your kingdom's operations and gain a competitive advantage."
The king thought about it for a moment, then without consulting his advisors, he said, "I'll do it! But where will I get all this data?". The wizard smiled and said, "Don't worry your Majesty. With enough gold coin, I'll take care of it." And with that, the wizard disappeared in a puff of smoke.
The next day, the king woke up to find a large team of data analysts locked away in a secret room in the tower. They had set up tables filled with charts and graphs and were busily inputting data and crunching numbers. The king was amazed. He saw that they were using data to make predictions about everything from weather patterns to crop yields.
The king asked if his advisors should be consulted since it was they who managed the affairs of the kingdom. The wizard replied “No need to bother them. They don’t understand this new alchemy, and it’s best that they focus on their daily choirs. All they really need is the final predictions.”
So the king’s court when about their daily business, blissfully unaware of what was happening in the secret tower room. His men-at-arms sharpened their swords, the chancellor counted the king’s coin, and the jester continued to jest. The kingdom’s future looked bright.
However, as time passed, the promised prosperity did not materialize. The king had emptied his treasury to fund his secret team and had little in return to show for it.
The predictions attempted to answer questions irrelevant to his kingdom’s prosperity. Results were presented to members of his court, but they didn’t know how to use the insights. The king grew deeply suspicious of the data team and the quality of the data they produced.
As his citizens went hungry, his army unpaid, and rival kingdoms closing in, the king finally realized his folie. In his eagerness to implement data analytics, he had created a siloed team that was completely cut off from his advisors in court and the realities of his kingdom. He had failed to understand that he needed to change his decision-making process and how the court operated.
In a last-ditch effort to save his crumbling kingdom, the king could be heard yelling “my kingdom, my kingdom for a Data-Driven Operating Model!”.
The wise wizard was nowhere to be found.
Today’s executives and management consultants tend to launch themselves enthusiastically into data-rich initiatives only to realize that elegant technical solutions are not sufficient to guarantee success. Leaders need to recognize that data is an asset with a financial value and attribute to it the appropriate importance within an organization. Aligning resources across the enterprise and balancing the needs for agility and governance is critical to any data-centric initiative.
A Data-Driven Operating Model is a framework to ensure internal and external customers obtain optimal value from enterprise data assets. It uses standards, templates, and best practices to minimize the proliferation of disjointed data silos and eliminates data congestion. It bridges the distance between edge users’ needs and business data investments while fostering greater levels of self-service data capabilities. Overall, it enables a company to operationalize data effectively.
Today’s Business Reality
It is all too common for me to hear executives express their exasperation about the inability of their data and analytics divisions to work together with other departments in the organization. Companies have spent lavish sums of money on recruiting professionals skilled in data analysis, but time after time, conversations revolve around a lack of collaboration between their data groups and the wider corporate environment.
“90% of firms are having difficulty scaling Analytics & AI across their enterprises, and data trust is the number 1 reason why” - Forester, 2020
More than half of the CxO respondents to a Forester 2020 survey admit that they simply don’t know what their AI & Analytics data needs are. Furthermore, firms struggle with ensuring data quality and data integration issues that leave them unable to connect multiple data sources. Without properly curated data, AI & Analytics initiatives are destined to fall short — which leads to increased costs, missed deadlines, and regulatory risks.
It's no secret that the success of data-first organizations lies in the ability to maximize their data. With a data-driven approach, businesses can expect better results across the board, from improved customer and employee experiences to increased innovation and sustained growth. All these benefits make it clear why having an effective use of data is so important.
Clearly, the advantages to data-driven insights in a business setting are unmistakable. But why do many organizations struggle with implementation? From my experience with such cases, it often boils down to an operating model that's not fit for purpose, one that stifles evidence and analysis under layers of bureaucracy and convoluted organizational structures.
To succeed in today’s environment, companies need to look beyond investing in the latest technology or hiring the hottest talent and focus on aligning their operating model.
Common Operating Gaps
A technology strategy that is unclear, with no links between the Enterprise Data Road Map and strategic business/tech objectives.
Misalignment between business and technology when it comes to important use cases.
A C-suite that’s not engaged or ready to drive cultural change and implementation of enterprise-wide best practices and priorities
Weak data governance model with no data quality standards.
No defined talent strategy to attract, retain, and grow data & analytics resources.
No clear decision-making process established between data teams and business divisions
An effective Data-Driven Operating Model can be the key to breaking down silos between business and technology. By determining the necessary enterprise-wide capabilities and objectives, it helps ensure that all relevant stakeholders, as well as appropriate technologies, are involved. When applied successfully, this type of operating model helps guide how the technology group performs its functions internally and with external businesses to create new value.
The Path Forward: Operational Alignment
Organizations should strive to ensure that senior-level executives are on the same page with regard to strategy, objectives, and enterprise and/or business metrics. Crafting a vision statement is essential in order to articulate the value of an Enterprise Data operating model, determine who its users will be, recognize what problems require addressing, and understand how it can provide value. Utilizing a data operating model like Eckerson's framework (Figure 1) can help companies ask pertinent questions when creating a vision statement.
Figure 1: High-level Operating Model for Data Analytics
Over the years, I have worked with some executives who have been successful in navigating the challenges of optimizing and aligning their organization’s operations to extract maximum value from their enterprise data assets. Repeatable themes in their approaches include:
1. C-suit Leadership. Data and analytics must be prioritized with sponsorship and necessary actions taken by the leadership team. It is essential for the C-suite to take responsibility in aiding data usage to make informed decisions, as well as build strong relationships between data scientists and business personnel.
2. Focus on what’s important. Rather than being overwhelmed by a flood of available data, successful companies surgically pinpoint business areas where analytical solutions can help optimize business procedures, forecast demand, and return maximum return on investment.
3. Placing Bets - It is important to allocate adequate funding for data and analytics initiatives and projects. Cross-functional funding strategies should span multiple functional areas including technology, digital, analytics, and customer success. Defined Measures of Success (MOS) and Critical Success Factors (CSF) can be leveraged to ensure progress and success.
4. Cross-team OKRs. Performance reviews and remuneration are linked to the effectiveness of collaboration between data teams and the business. Cross-team OKRs can be used to accelerate results from enhanced understanding and cooperation among teams. Sharing dependent OKRs and reviewing areas of mutual interest opens the door to an enhanced understanding of each function and creates opportunities for mutual support.
5. Approach talent differently. Finding the right data experts is critical for any business's success, and those from digital-savvy companies like Amazon or Netflix are especially valuable. For those experts who move into the realm of more traditional industries, their knowledge is immediately relatable after a short transition period. Additionally, businesses should consider investing in training for all employees across all levels so that there is an enhanced appreciation of data and its importance.
6. Attract, Retain, and Grow Talent - With the pandemic, economic instability, and the Great Resignation, the war for key talent has intensified. Even more so in the indispensable Data & Analytics arena where top talent is bilingual in data technology and business strategy. In these higher-complexity positions, stronger performers have an increasingly disproportionate bottom-line impact.
Today, as more companies use AI and other tools to tap into the data-driven insights they need to stay competitive, effective managing of data assets is imperative. Companies need to democratize their data, enable people on the edges, and empower data-driven decision-making, all while staying in sync with corporate strategy. By investing in these areas, companies can position themselves to compete and succeed in today's data-driven economy.
"To succeed, companies need to look beyond investing in the latest technology or hiring the hottest talent and recognize the financial value of their data and align their organization to take advantage of it." - John Frankovich, IQ Innovation.
About IQ Innovation
Differentiating your Professional Services Organization can be a challenge. You must find new ways to grow revenue, improve customer satisfaction, and optimize operations. At the same time, you're competing with other organizations that are also searching for innovative solutions. That's where we come in.
At IQ Innovation (IQI), we partner with Professional Service leaders to develop and execute tailored solutions that help them achieve their business goals. We take a hands-on approach to collaboration with our clients to identify new revenue streams, grow customer demand, improve CSAT scores, increase margins, and maximize profitability.
Media Contact Details:
Business Name: IQ Innovation
Name: John Frankovich