Let me start by introducing you to Operations
Innovation has completely changed how Business used to happen 50 years from now and how it is happening now. This Innovation which we term as Disruption is driving 100-year-old, blue-chip companies out of business. Start-ups that rethink basic services like hotels and taxis are disrupting entire industries. In this age, Innovate or die is the business mantra of the day. But this Innovation has to be strategic and specific to Business Function, which can either be Finance, IT, HR or Operations. Well I am in Operations, so my article is related to that. Operation who work hand in hand with Finance, sales and HR have made their presence felt by achieving high customer satisfaction, speedy and efficient delivery, along with great cost-effectiveness. When you verbify Operate, it becomes operations. Its Operation Management can be stated as function that enables organizations to achieve their goals through efficient acquisition and utilization of resources. Effective Operation management has always rescued various corporates from Billions of Loss’s. If you do not believe, just open your browser and google case study on how Six Sigma helped its inventor at Motorola achieve his objective of saving millions and how that success inspired GE to embrace. But how and when did this happen? Curious to know!!
Evolution in Operation, which was initiated when importance of Data was realized.
It all began during Industrial revolution, when Adam Smith recognized the economic benefits of specialization of labor. It was followed by research work of Engineers like Frederick W. Taylor, Frank & Lillian Gilbrethand, Henry L Grant introducing the concept of ‘‘Scientific Management’’ in Operation Management. All these events were just the beginning as they paved way for further research and inspired Statisticians like H.F. Dodge and H.G. Romig to come up with statistical quality control. During 1970 It was realized that Quality should be further enhanced, which led to invention of techniques like JIT and TQM. All these events had some similarity, their innovation were based on how to utilize information effectively, information which was derived from processing data. What mattered was the change, it was no longer “who made those decision, what data was factored into making those decision became equally critical. While Digital age (Which is itself fueled by data) gifted computer driven inventions like MRP which followed by ERP, which took leverage of data to a new level by enhancing how data was managed to achieve desired result.
MRP if you expand becomes “material resource planning” which were meant for what and how many materials you’ll need, used exclusively to help manage manufacturing processes and their production planning with these systems.
ERP (abbreviation of enterprise resource planning) not only did was MRP was capable of doing but had enhanced functionality of managing and integrating data from various modules like HCM, CRM ,SCM and EPM.
Late 20th century which marks the transient from industrial age to Digital age, triggered a shift from the manufacturing industry to service industry which relied on creativity and knowledge as its wheels. You can’t be creative without information and Knowledge both are end product of data.
But let me point our Digital age is just an onset of Information Age which will be fuelled with Data and only those who know how to use it can survive and thrive in this age.
I am moving to the main theme of my article Data and Data Governance.
Data was not discovered during Industrial age but was there from the beginning, but I agree it was only during industrial age it was realized that it can be leveraged for more than just record keeping. During industrial age, when data was integrated statistics, management consultants were able to come up with a forecasting model. While in Digital age, data helped managers understand their company product and decide what more needs to be done. Information age will see value of Data reach its peak. But an asset like this also needs maintenance and protection as well. This need resulted in birth of data governance. Governance which implies establishing as policies and mechanism along with continuous monitoring for achieving prosperity and viability of organization. Same definition when applied on data defines Data governance.
“Data Governance is a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.”
Data Governance should not be confused with Information Governance as former is a holistic approach to managing corporate information by implementing processes, roles, controls and metrics that treat information as a valuable business asset.
I’ll present few scenarios where context varies to explain you data governance is about
- Who will be part of the Governance council formulating the policies and standards?
- What will be those policies, standards and guidelines?
- Setting up a plan and accountabilities on how and who will be enforcing those policies, standards and guideline.
- Decide who will be owners or custodians of the data assets in the enterprise who will be called data stewards.
It will deliver a lot if you inculcate….
Minimize risks and improve reliability, accountability and trustworthiness.
Streamline flow of data within organization compliance requirement
Help in understanding data and where automation can replace manual work and eventually resulting in low cost.
Better Understanding of End-to-End data lineage for better traceability of data sources.
Communication which has data at its base will improve drastically because of common data governance platform.
At a Data Governance Conference in Orlando, Florida (USA) in December, 2006, a group of managers of successful Data Governance programs reached a startling consensus: They agreed that Data Governance is actually somewhere between 80 and 95% communications!
Effectiveness to communicate with stakeholders and stewards that makes a Data Governance program successful.
Recent regulatory compliance acts like GDPR, BCBS 239, CCAR, Dodd-Frank and MiFID which put onus on Firms to be put into proper data management practice. Proper data governance burden can ease risk assessment burdens
It will also help in deciding what data needs to be siloed(Kept in isolation) and what can be accessed by teams outside team.
Decide which data elements are critical and will not qualify for automated flow and but will have to take the manual route.
Achieving continuous optimization of the organization within the risk framework
There is Strategy to Manage Data too!!
Strategy which can be explained as an action that managers take to attain one or more of the organization’s goals, which has to be coherent and aligned with Business objective. When the same concept applied for managing data becomes data strategy.
Data strategy is set of policies and course of action which will be taken to manage you data aligned with you’re company’s objective. Absence of it will lead to missing out on identifiable business objectives. Data strategy is classified into two – Defensive and Offensive
A defensive strategy focuses on control and standardization by being regulating and compliance centric, placing check to stop any data breach. Management of Data which generates from legal, financial, compliance, and IT concerns has to follow this strategy.
While an offensive strategy focuses on flexibility and is customer centric, where focus is on increasing revenue, profitability, and customer satisfaction. When you are focused on sales and marketing you can switch to offensive strategy.
Both strategy has to applied, and there has to be balancing act. Its Chief Data Officer’s responsibility is to decide where to do it.
Fusion of Four domains which to achieve data governance.
- Data stewardship: A council of data steward who are custodians of Customer data coming together taking ownership for management through data lineage(Data lifecycle). This data steward community is diverse as it comprises of stewards from all Business domains with expertise in those domains. It is they who keep a track of its data movement and changes. It’s a tactical role who work for achieving the target.
- Data Quality: It is KPI to measure integrity and credibility of Data in you’re hand after data scrubbing and data profiling is done. Data integrity of the data inputs must be high, or else the outputs from the system will be incorrect. If it’s a metric there is check list, which includes.
- Completeness: Whether any values are missing?
- Validity: Measure what it claims to measures
- Uniqueness: Eliminating redundancy
- Accuracy: How much it is close to actual numbers
- Timeliness: Will Forecasted data match actual data at specific point of time?
- Master data management: Metadata repository of data about data. Documenting where data resides along with identifying, defining, and classifying data within subject areas. There are two Docs one for internal users and other for external one, depending on what should be siloed and what should be open to the world.
- Business intelligence: BI Tools which transform raw data into meaningful and actionable information to improve your business. They are not just meant to deliver insight but maintain oversight and visibility too. Both of these are must for the data governance.
I’ll conclude with the most important part, how to Gauge it.
How do you convince Management and get them on board, obviously with numbers in your hand. I have explained you what DG means and what it will do. Metrics is what needed to justify existence of any Business unit. Well if you are measuring effectiveness of data governance it should measure.
- How swiftly Communication of Data related updates are happening.
- How effective is DG as First line of defense against any risk assessment and audit.
- To what extent the picture of the customer becomes clearer to sales and marketing team after DG is applied on it.
I’ll end by stating Governance which gets noticed when it is absent or something is wrong.