The impact of data management and data governance on a business – particularly as it evolves to become a digital enterprise – is potentially huge, as data supports decisions, processes, customer experiences and new business models. Last year was a big year for data management with the introduction of global privacy legislation to mandate how organisations need to protect and use their data. However, let’s face it, data management can have the reputation as being boring, tedious and complex. Data, once relegated to individual business groups for their internal business applications, now needs a carefully planned, modernised data platform to deliver on the promise of deep, transformative insights across the enterprise.
Despite how much the data management landscape has already evolved, it has now become even more business critical to get right. Firstly, let’s recap what data management and data governance actually covers.
What is Data Management & Data Governance?
Data governance is about understanding your data whether structured or unstructured and governing it to mitigate compliance risks and empower your information stakeholders. It’s about the overall management of data availability, relevancy, usability, integrity and security in an enterprise. It helps organisations manage their information knowledge and answer questions, such as:
- What do we know about our information?
- Where did this data come from?
- Does this data adhere to company policies and rules?
Data governance defines how data is accessed and treated within a broader data management strategy and relates to the way data is managed and protected as an asset.
Data management on the other hand is the implementation of architectures, tools and processes to achieve stated data governance objectives. Data management ensures that all data is collected, structured, organised and stored in appropriate ways. Data governance is an important sub-branch of data management. The umbrella of data management covers the following:
- Data governance
- Data architecture
- Data modelling & design
- Database & storage management
- Data security
- Reference & master data
- Data integration & interoperability
- Data warehousing & business intelligence
- Metadata management
- Data quality
What’s Driving A Renewed Focus On Data Management?
1. Data Protection & Data Transparency
Driven by strong demands from consumers, enterprises will have to accommodate the need for increased and improved data protection across platforms, social media channels, cloud applications and others, while also meeting the increased calls for transparency regarding how data is collected, aggregated and shared.
In an era of emerging regulations like GDPR, the evolving Australian Privacy Act 1988, and high-profile data leaks such as the Facebook scandal and the Australian National University breach by Chinese hackers in July last year, data governance has become a top priority for data executives. In Australia alone in 2018 more than 800 data breaches were reported to the Office of the Australian Information Commissioner (OAIC) – the Australian privacy watchdog, with the health sector the leading source of breaches.
It’s no longer enough to just ‘manage’ your data records effectively, it’s now important for businesses to govern who can access and use data assets. An effective data governance framework helps businesses ensure that they have the necessary privacy and information security controls in place when moving or giving access to data, especially important if you’re operating a self-serve business analytics approach.
Organisations should be prepared for more regulations centred on consumer data protection, with the associated changes to technology needed to cope. This means it will be critical for organisations to find the correct balance of being agile and exploratory with data and technology with governance and control of data approaches to solving data-centric problems.
2. Data Management + Advanced Analytics = Unlocking Big Data Insight
Companies are increasingly looking at variations of data lake concepts that combine Hadoop Distributed File System infrastructure, event stream processing, relational and non-relational data stores, and other technologies. Further, it’s not necessarily data volume in itself that poses the biggest challenges. Inexpensive technology to process billions of transactions is commonplace but extracting value and insights from that data (good or bad) can be difficult. Advanced analytics paired with good data management technology can help detect threats and uncover untapped opportunities. As more organisations attempt to use Artificial Intelligence (AI) and machine learning (ML) techniques to improve data quality and data management processes, similarly, more companies will look to automation (often supplemented by AI and ML technologies) to help scale data management processes without adding substantially more staff. By using advanced analytics paired with automation, organisations will start to ease the burden on overworked data engineers and data stewards.
3. The Need To Be More Productive & Cost Effective
According to a data governance IDC report released last year, organisations lack data knowledge for efficient and effective data governance activities, with 30% of time spent on data governance actually wasted. In fact, the research suggests employees are wasting up to 12 hours per week because they cannot find, prepare or protect data. Savvy CIO’s are revitalizing data governance with a streamlined approach that tells a more attractive story about producing data that the organisation can trust and use, to build competitive advantage.
The Power Of Reliable Data
Building and implementing a strong data governance and data management strategy has many tangible benefits, it:
- Provides a strong foundation for business analytics & fact-based decision making
- Enforces roles and accountability for data management
- Maintains regulatory compliance
- Minimises data loss & therefore potential lost business opportunities
- Standardises data management and utilisation
- Increases productivity, reduces inefficiencies & costs if data is well organised, easy to find and access
- Improves data security by defining and verifying the requirements for data access
- Establishes processing for data access to improve performance
- Improves data quality by automatically cleaning & organising data, removing duplicates
We’ve often found that there are three over-arching mandates that form the basis for building a common set of standards, processes, patterns and tooling for data management and governance of data assets. These are to:
- Lower data management costs and risks by protecting data & reducing the duplication of effort & data across systems
- Improve decision making by reducing the risk of basing decisions on false or inaccurate data
- Provide a modern data architecture that improves productivity & enables deeper analytics such as predictive and prescriptive insights
Forging consensus for a new data management framework usually involves 4 key phases. Firstly, obtaining executive sponsorship, then shifting the culture to include data and analytics design principles – this is a big one. Then thirdly investing in missing data management services, this may involve engaging external data management specialists like BOOMDATA, to produce a scalable data platform design & deliverables and finally, aligning and tracking adoption across the enterprise to measure success. When looking at such an initiative key priorities and design recommendations may include something like the following (these are examples):
Wherever your capabilities lie on the data management and data governance spectrum, one thing is certain, CIO’s now need a carefully planned, modernised data management strategy with supporting architecture and technical specialists to deliver on the promise of information security and deep, transformative insights.
Learn more about your data management processes and how they’re working for your business by reaching out.
