Strategic Business Leader · Big data and data analytics

Managing and Governing Data in the Organisation

Updated 11 October 2026 · Fact-checked

Managing and governing data means setting clear rules, roles and controls so that data is accurate, secure, lawful and useful. In SBL you answer by linking governance, data quality, storage, skills and strategy to the scenario, then advising how the organisation can exploit big data to create value.

Understand Managing and Governing Data Within the Organisation

Data is only valuable if people can trust it and use it. Many organisations collect large amounts of data but cannot turn it into decisions. Managing and governing data closes that gap.

Data governance is the framework of policies, standards, roles and processes that controls how data is collected, stored, used, shared and deleted. It sets who owns the data, who may access it, and who is accountable when something goes wrong. It should sit within overall corporate governance and risk management. The board is responsible for it, not just the IT department.

Data quality decides whether analysis can be relied on. Poor data gives poor decisions, however clever the analytics. Typical quality dimensions are accuracy, completeness, consistency, timeliness, validity and uniqueness. Big data makes this harder because it comes from many sources, in many formats, often unstructured and unverified. Think of social media posts, sensor feeds and third-party data.

Storage and skills are the practical enablers. Traditional databases and data warehouses hold structured data in a set format. A data lake holds large volumes of raw data in its original form, structured or not, until it is needed. Cloud storage lets an organisation rent scalable capacity instead of buying its own servers. It lowers upfront cost and scales quickly, but raises issues of security, location of data, supplier dependence and legal compliance. People matter as much as systems. Organisations need data scientists, analysts and managers who can ask the right questions and interpret results, and many struggle to hire or keep them.

Strategy ties it together. Without a clear data strategy, organisations drift into collecting data with no purpose. A good strategy starts from business objectives, decides which questions data should answer, then sets out the data, technology, skills, governance and investment needed. It should be tested for costs, benefits, risks and ethics, including privacy and data protection law. In SBL you must apply all this to the case, not list theory.

Key rules to remember

Data governance framework elements: Policies + Standards + Roles/Ownership + Processes + Controls + Monitoring; Use as a checklist when advising on governance. Add accountability at board level.
Data quality dimensions: Accuracy, Completeness, Consistency, Timeliness, Validity, Uniqueness; Not a fixed official list. Pick the ones that matter in the scenario and explain the effect on decisions.
Data strategy building blocks: Objectives → Data needed → Technology and storage → Skills → Governance → Ethics and compliance → Costs and benefits; A logical order for an answer. Start with business objectives, not technology.

How to solve Managing and Governing Data Within the Organisation questions

Use this method for any SBL task on managing, governing or exploiting data. It keeps your answer applied and gets professional skills marks.

1Read the requirement and note the verb and role (advise, evaluate, recommend, report to the board).
2Identify the business objective in the scenario that data should support, such as better customer insight or lower costs.
3Pick the relevant area: governance, quality, storage, skills or strategy. Often the task needs two or three.
4For each area, state the issue briefly, then link it to specific facts from the case, such as data sources, systems and staff.
5Explain the consequence for the organisation: poor decisions, legal breach, cost, lost opportunity or reputational damage.
6Give practical recommendations with owners, for example appoint a data owner, set quality checks, use cloud with safeguards, train staff.
7Balance benefits against costs, risks and ethical or legal issues, then give a clear conclusion in the required format.

Quickest way: G-Q-S-S-S checklist

When to use it: When you have little planning time and need a structure fast for a data-related task.

G: Governance. Who owns, controls and is accountable for data?
Q: Quality. Is the data accurate, complete, consistent and timely?
S: Storage. Warehouse, data lake or cloud, and the security and legal risks.
S: Skills. Do we have people who can analyse and interpret data?
S: Strategy. Does a clear plan link data to business objectives, costs and benefits?
Write one case-specific point per letter, then add a recommendation and conclusion.

Common mistakes in Managing and Governing Data Within the Organisation

Treating data governance as an IT issue only.
Students link data with technology and forget it is a board and management responsibility.
Fix: Name the board, data owners and senior management as accountable, and link governance to risk and corporate governance.
Listing the Vs of big data or definitions without applying them.
Students recall theory from the earlier topic and write it out to fill space.
Fix: Only use definitions to support a point about the case. Every paragraph should refer to the organisation's facts.
Ignoring data quality and assuming more data means better decisions.
Big data is presented as an opportunity, so the downsides are overlooked.
Fix: Explain that unreliable, inconsistent or biased data leads to wrong conclusions, and recommend validation and cleansing controls.
Recommending cloud storage or data lakes without discussing risks.
Students see cloud as simply cheaper and more flexible.
Fix: Cover security, data location, legal compliance, supplier reliance and loss of control alongside the benefits.
Forgetting skills and culture.
Students focus on systems and ignore people.
Fix: Mention shortages of data skills, training, hiring and a culture that trusts and uses data in decisions.
Giving generic recommendations with no priorities or conclusion.
Time pressure leads to a list of ideas with no judgement.
Fix: Prioritise two or three actions, justify them for the case, and end with a clear recommendation.

Worked examples

Example 1

A retail chain collects customer data from loyalty cards, its website, social media and store sensors. Each department holds its own data and uses different formats. Managers say the data is unreliable and rarely used in decisions. As an adviser, explain the data governance and data quality problems and recommend improvements.

Show the solution

Governance problem: no single ownership. Each department holds its own data, so there are no common standards, and nobody is accountable for accuracy, access or use.
Quality problem: different formats and sources lead to inconsistent, duplicated and possibly incomplete records. Social media data may also be unverified.
Consequence: managers distrust the data, so they do not use it. The chain loses insight into customers and may make poor decisions or breach data protection rules.
Recommendation 1: set up a data governance framework approved by the board, with a senior owner and named data owners in each department.
Recommendation 2: adopt common data standards and definitions, and add validation, cleansing and regular quality checks.
Recommendation 3: consolidate data in a central store, such as a data lake or warehouse, with access controls, so there is one version of the truth.
Recommendation 4: train managers to use the data and report quality measures to the board.
Conclusion: governance and quality must be fixed first, otherwise further investment in analytics will not produce reliable value.

Answer: The chain lacks ownership, standards and quality controls. Introduce board-backed governance, common standards, validation and central storage with access controls, and train managers. Trust in the data must be restored before analytics can add value.

Example 2

The board of a logistics company wants to exploit big data from vehicle sensors to cut fuel costs. It plans to move all data to a cloud data lake, but has no data scientists. Advise the board on what a data strategy should include and the risks of its plan.

Show the solution

Start with the objective: cut fuel costs. The strategy should state which questions the data must answer, such as which routes or driving behaviour waste fuel.
Data: identify the sensor data needed and check its quality, such as accuracy and completeness of readings, before large investment.
Storage: a cloud data lake suits large volumes of raw sensor data and scales cheaply, with low upfront cost.
Storage risks: security of the data, where it is held and legal compliance, reliance on the supplier and costs that grow with use.
Skills: with no data scientists, analysis will not happen. Options are hiring, training existing staff or using external specialists, each with cost and retention issues.
Governance: assign data ownership, access rules and monitoring. Consider privacy, for example data that tracks individual drivers.
Costs and benefits: compare the investment in storage, tools and skills with expected fuel savings, and start with a pilot.
Conclusion: the plan is incomplete. Approve it only if it adds skills, governance and a clear link to the fuel-saving objective.

Answer: The strategy should link data to fuel savings and cover data quality, storage, skills, governance, privacy and costs. The cloud data lake is useful but risky, and without data skills and governance the plan will not deliver benefits. Recommend a pilot with added skills and controls.

Exam tips

Always tie each point to a fact in the case. Generic data theory earns few marks in SBL.
Cover people and governance as well as technology. Examiners often reward the point about missing skills or ownership.
Give balanced advice: benefits, then risks such as quality, security, legal compliance and ethics, then a clear recommendation.
Use the format asked for, such as a report or briefing note, with a short conclusion. This supports professional skills marks.
Use your pre-seen planning time to note the organisation's data sources, systems and any data problems that could appear in the exam tasks.

Practice questions from Big data and data analytics

Managing and Governing Data Within the Organisation: frequently asked questions

What is data governance in simple terms?

It is the set of rules, roles and controls that decide how data is collected, stored, used, shared and deleted. It makes someone accountable for data accuracy, security and lawful use. It should be overseen by the board.

What is the difference between a data warehouse and a data lake?

A data warehouse holds cleaned, structured data organised for reporting. A data lake holds large volumes of raw data in its original form, including unstructured data, until it is needed. Lakes are flexible but need good governance to avoid becoming unusable.

Why does data quality matter for big data analytics?

Analytics can only be as good as the data fed into it. Inaccurate, incomplete or inconsistent data leads to wrong conclusions and poor decisions. Big data often comes from many unverified sources, so quality controls are essential.

How do I implement a big data strategy in an organisation?

Start with business objectives and the questions data should answer. Then decide on data sources, storage, skills, governance and ethical and legal safeguards, and weigh the costs and benefits. A pilot project is often a sensible first step.