I’m more into eating than cooking but I related with this sentence from the Michelin Guide website.
Great cooking starts with great ingredients, so use the best produce you can find — whether that’s a tomato or a chicken.
This relates deep into what I’ve been stating along the time about data and being data driven. Just out of curiosity lets look at this more closely.
What is a Michelin Star?
According to the Michelin Guide website these are the criteria for the Michelin Stars attribution:
A Michelin Star is awarded for outstanding cooking. We take into account the quality of the ingredients, the harmony of flavours, the mastery of techniques, the personality of the chef as expressed in their cuisine and, just as importantly, consistency both over time and across the entire menu. 
What is a Data-Driven Star?
How do these cooking criteria relate to data? What should organization focus on to get this hypothetical Data-Driven Star?
“The quality of the ingredients” (Data Quality)
Data is the biggest challenge when employing new technologies and the impact of data-driven decisions is larger than ever before. The availability of high-quality data is paramount for technology to have the right impact.
Owning and trusting data are two completely different things, the amounts of data available are increasingly bigger however that data is not suitable or trustable enough to make business-critical decisions with. This lack of trust in the data has a twofold impact, it either inhibits decision making, or even worse, it induces decisions based on false assumptions. This can only be solved by developing strategies to address existing and ongoing issues and implementing a methodology to execute on that strategy.
A data quality framework encompassing data quality discovery, remediation, and monitoring can help organizations approach the data quality issues that hinder the success of analytics or machine learning initiatives.
This approach allows data quality processes within the organization to be performed in a more agile and scalable way, reducing the siloed approach and manual interventions. Resulting in an overall improvement of the value returned from analytics or machine learning.
Considering that data is one of the organizations most critical assets, making data quality the basis of data related initiative, leverages the value of data and unlocks new insights critical to create the necessary edge on an increasingly competitive business ecosystem.
“The harmony of flavours” (Data Governance)
Data governance may not seem to be the highest priority. But this is the time to address issues that have always existed in all types of organizations:
- The gap between business and IT, leading to often contradictory strategies.
- Difficulty in managing a siloed data ecosystem.
- Difficulty to identify and define data across sources.
- Lack of standard business and data management rules and data protection policies.
- Rising data-security concerns around providing employees with remote access to data.
- Difficulty to identify, cleanse, standardize, and curate data for sharing.
- Existence of duplicate, erroneous, inaccurate, and incomplete data.
With one common denominator: data. How to make all this work together? Data Governance.
From day one, data is being created, compiled, collected, stored, and distributed. Data is present in all the organization’s processes, from risk or regulatory compliance to routine operations.
Organizations are investing heavily in leveraging new technologies, artificial intelligence, machine learning the internet of things, augmented and predictive analytics, and data is at the core of each of these initiatives.
They are fully dependent on data; all are aimed at providing quality data that is essential to improving insights and driving substantiated business decisions.
“The mastery of techniques” — Skills and technology
Usually, we relate everything data related with data as technologically driven, it is, in a way, but beyond technology, and no matter what context, the technological solutions and the necessary talent to work the technology are always available.
There are skills far more critical for success in this kind of transformation, business skills, the skills that are often left out of the process.
Data is frequently referred as a business or corporate asset, although rarely treated one.
Data seems to be a world apart.
The fact is that, as any other asset in the organization data’s purpose is to create value, data exists to support business, so any data strategy must be oriented towards the organization’s strategic priorities and key business objectives.
It’s not possible to talk about data-driven businesses without a full commitment to business-driven data.
An approach from a strictly technological perspective, overlooks that the ultimate purpose is to leverage data to generate business value. If there’s a perspective, it’s the business perspective.
All initiatives must be driven and oriented by the business units.
Creating a data driven organization is not an IT function, it is a business function, it is the business who better knows what their problems and objectives are. The role of IT in this process is to find the right technology and support the business units in this journey.
“The personality of the chef” — Leadership
Organizations start their transformations from the top, so top management shouldn’t only be on top of this change, they should be in front of these changes, modelling the new habits and holding each other’s accountable for its success.
These are processes that need buy-in from every level of an organization, and it starts with strong executive sponsorship but also from every other stakeholder in the organization, which need to be aligned and committed to the program.
Frequently identified as a cause for data initiatives failures, the lack of leadership buy-in and commitment can be tracked back to the absence of a strong vision.
The role of top management it’s not simply to sanction a digital transformation.
It is essential that they communicate a vision of what and why is to be achieved — a strong purpose — to demonstrate that the transformation is an unquestionable priority, making other leaders accountable, and making it harder to back-track.
These processes need buy-in from all levels of the organization, it may start with strong executive sponsorship but needs the commitment of all the other stakeholders in the organization.
“Consistency both over time and across the entire menu” — Data Strategy
Data can be the most powerful asset an organization has; however, it is still an under-managed and under-exploited asset. Having a comprehensive data strategy gives organizations a substantial competitive advantage, laying out a comprehensive vision across the organization and incorporating the guiding principles to accomplish the data-driven vision, direct the organization to specific business objectives, acting as a starting point for data-driven planning.
A data strategy must answer the question: How will data enable the business strategy?
A data strategy will act as a guide and aggregator of every data initiative, and because data related business needs and requirement exist and need to be addressed, we witness the spread of multiple, independent, and uncoordinated initiatives — be it data analytics, business intelligence, data science initiatives, or even data governance, master data or data quality initiatives — that address contextual, circumstantial needs, driven by different business areas, without an integrated perspective. Initiatives that most often than not will fall short of its objectives.
Being, as I am, a strong advocate of focused, targeted data initiatives driven and oriented by business units — pretty much like the ones described above — I must also be a strong advocate of a structured and comprehensive data strategy, grounded on strategical business objectives, designed, and implemented at executive level, as a foundation each data initiative, everything data dependent or data related within the organization.
Go for your Data-Driven Star
Organizations that meet the criteria above will definitely get their Data-Driven Stars.
Moving into data-driven business models is crucial for organizations to thrive in increasingly complex, dynamic, and competitive markets.
Becoming a data-driven creates the capability to generate new efficiencies within existing business models, optimizing assets and resources, reducing costs, increasing profitability, improving customer engagement, and enabling new business models.
Being data-driven enables organizations to act effectively and swiftly to increasing demands from their business, their customers, and the market.
Driving innovation with data, investing on analytics, managing data as a business asset, developing well-articulated data strategies, and creating a data culture are priorities.
Most organizations that started this journey are still far from this reality.
It is important to be aware that for most organizations the results of digitalization processes fall short from the objectives and will settle for dilution of value and mediocre performance, confronted with a situation where they simply assume that the investment was wasted and worse than that, accept to live with mediocre, under-performing solutions — expensive failures.
Deriving value from the investment needed to become data-driven is a challenge, for organizations locked in legacy data environments, business processes, skill sets, and change resistant cultures, as they struggle to enable their data capabilities.
The transformation process that leads to a data-driven organization must be wholeheartedly supported on the business strategy and objectives — not on technology. The purpose is to create business value, so the transformation strategy, must be oriented towards the organization’s strategic priorities and key business objectives.
Business must have the prerogative to determine what are the priorities and objectives of the transformation, especially when most of the transformations tend to be customer oriented, and who better that the business to have the necessary awareness and knowledge of the customers’ expectations.
More than a data-driven transformation this is a business-driven transformation — where all initiatives are oriented by the business units and grounded on clear business use cases — aligned with strategical business objectives.