Tag: data storage

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erwin Expert Blog

Data Governance 2.0: The CIO’s Guide to Collaborative Data Governance

In the data-driven era, CIO’s need a solid understanding of data governance 2.0 …

Data governance (DG) is no longer about just compliance or relegated to the confines of IT. Today, data governance needs to be a ubiquitous part of your organization’s culture.

As the CIO, your stakeholders include both IT and business users in collaborative relationships, which means data governance is not only your business, it’s everyone’s business.

The ability to quickly collect vast amounts of data, analyze it and then use what you’ve learned to help foster better decision-making is the dream of business executives. But that vision is more difficult to execute than it might first appear.

While many organizations are aware of the need to implement a formal data governance initiative, many have faced obstacles getting started.

A lack of resources, difficulties in proving the business case, and challenges in getting senior management to see the importance of such an effort rank among the biggest obstacles facing DG initiatives, according to a recent survey by UBM.

Common Data Governance Challenges - Data Governance 2.0

Despite such hurdles, organizations are committed to trying to get data governance right. The same UBM study found that 98% of respondents considered data governance either important, or critically important to their organization.

And it’s unsurprising too. Considering that the unprecedented levels of digital transformation, with rapidly changing and evolving technology, mean data governance is not just an option, but rather a necessity.

Recognizing this, the IDC DX Awards recently resurfaced to give proper recognition and distinction to organizations who have successfully digitized their systems and business processes.

Creating a Culture of Data Governance

The right data of the right quality, regardless of where it is stored or what format it is stored in, must be available for use only by the right people for the right purpose. This is the promise of a formal data governance practice.

However, to create a culture of data governance requires buy-in from the top down, and the appropriate systems, tools and frameworks to ensure its continued success.

This take on data governance is often dubbed as Data Governance 2.0.

At erwin, we’ve identified what we believe to be the five pillars of data governance readiness:

  1. Initiative Sponsorship: Without executive sponsorship, you’ll have difficulty obtaining the funding, resources, support and alignment necessary for successful DG.
  2. Organizational Support: DG needs to be integrated into the data stewardship teams and wider culture. It also requires funding.
  3. Team Resources: Most successful organizations have established a formal data management group at the enterprise level. As a foundational component of enterprise data management, DG would reside in such a group.
  4. Enterprise Data Management Methodology: DG is foundational to enterprise data management. Without the other essential components (e.g., metadata management, enterprise data architecture, data quality management), DG will be a struggle.
  5. Delivery Capability: Successful and sustainable DG initiatives are supported by specialized tools, which are scoped as part of the DG initiative’s technical requirements.

Data Security

Data is becoming increasingly difficult to manage, control and secure as evidenced by the uptick in data breaches in almost every industry.

Therefore companies must work to secure intellectual property (IPs), client information and so much more.

So CIOs have to come up with appropriate plans to restrict certain people from accessing this information and allow only a small, relevant circle to view it when necessary.

However, this job isn’t as easy as you think it is. Organizations must walk the line between ease of access/data discoverability and security.

It’s the CIO’s responsibility to keep the balance, and data governance tools with role-based access can help maintain that balance.

Data Storage

The amount of data modern organizations have to manage means CIOs have to rethink data storage, as well as security.

This includes considerations as to what data should be stored and where, as well as understanding what data the organization – and the stakeholders within it – is responsible for.

This knowledge will enable better analysis, and the data used for such analysis more easily accessed when required and by approved parties. This is especially crucial for compliance with government regulations like the General Data Protection Regulation (GDPR), as well as other data regulations.

Defining the Right Audience

It’s a CIO’s responsibility to oversee the organization’s data governance systems. Of course, this means the implementation and upkeep of such systems, but it also includes creating the policies that will inform the data governance program itself.

Nowadays, lots of employees think they need access to all of an organization’s data to help them make better decisions for the company.

However, this can possibly expose company data to numerous threats and cyber attacks as well as intellectual property infringement.

So data governance that ensures only the right audience can access specific company information can come in handy, especially during a company’s brainstorming seasons, new products and services releases, and so much more.

Data governance is to be tailored by CIOs to meet their organizations’ specific needs (and wants). This is to ensure an efficient and effective way of utilizing data while also enabling employees to make better and wiser business decisions.

The Right Tools Help Solve the Enterprise Data Dilemma

What data do we have, where is it and what does it mean? This is the data dilemma that plagues most organizations.

The right tools can make or break your data governance initiatives. They encompass a number of different technologies, including data cataloging, data literacy, business process modeling, enterprise architecture and data modeling.

Each of these tools separately contribute to better data governance, however, increasingly, organizations are realizing the benefits of interconnectivity between them. This interconnectivity can be achieved through centralizing data-driven projects around metadata.

This means data professionals and their work benefits from a single source of truth, making analysis faster, more trustworthy and far easier to collaborate on.

With the erwin EDGE, an “enterprise data governance experience” is created to underpin Data Governance 2.0.

It unifies data and business architectures so all IT and business stakeholders can access relevant data in the context of their roles, supporting a culture committed to using data as a mission-critical asset and orchestrating the key mechanisms required to discover, fully understand, actively govern and effectively socialize and align data to the business.

You can learn more about data governance by reading our whitepaper: Examining the Data Trinity: Governance, Security and Privacy.

Examining the Data Trinity - Governance, Security and Privacy

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erwin Expert Blog

Data management tools essential for building the data foundation platform

Instead of utilizing built for purpose data management tools, businesses in the early stages of a data strategy often leverage pre existing, make-shift software.

However, the rate in which modern businesses create and store data, means these methods can be quickly outgrown.

In our last post, we looked at why any business with current, or future plans for a data-driven strategy need to ensure a strong data foundation is in place.

Without this, the insight provided by data can often be incomplete and misleading. This negates many of the benefits data strategies are typically implemented to find, and can cause problems down the line such as slowing down time to markets; increasing the potential for missteps and false starts; and above all else, adding to costs.

Leveraging a combination of data management tools, including data modeling, enterprise architecture and business processes can ensure the data foundations are strong, and analysis going forward is as accurate as possible.

For a breakdown of each discipline, how they fit together, and why they’re better together, read on below:

This post is part two of a two part series. For part 1, see here.

Data management tools for an agile data foundation.

Data Modeling

Effective Data Modeling helps an organization catalogue, and standardize data, making the data more consistent and easier to digest and comprehend. It can provide direction for a systems strategy and aid in data analysis when developing new databases.

The value in the former is that it can indicate what kind of data should influence business processes, while the latter helps an organization find exactly what data they have available to them and categorize it.

In the modern world, data is a valuable resource, and so active data modeling in order to manage data, can reveal new threads of useful information. It gives businesses a way to query their databases for more refined and targeted analysis. Without an effective data model, insightful data can quite easily be overlooked.

Data modeling also helps organizations break down data silos. Typically, much of the data an organization possesses is kept on disparate systems and thus, making meaningful connections between them can be difficult. Data modeling serves to ease the integration of these systems, adding a new layer of depth to analysis.

Additionally, data modeling makes collaborating easier. As a rigorous and visual form of documentation, it can break down complexity and provide an organization with a defined framework, making communicating and sharing information about the business and its operations more straightforward.

Enterprise Architecture

Enterprise Architecture (EA) is a form of strategic planning used to map a businesses current capabilities, and determine the best course of action to achieve the ideal future state vision for the organization.

It typically straddles two key responsibilities. Those being ‘foundational’ enterprise architecture, and ‘vanguard’ enterprise architecture. Foundational EA tends to be more focused on the short term and is essentially implemented to govern ‘legacy IT’ tasks. The tasks we colloquially refer to as ‘keeping on the lights’.

It benefits a business by ensuring things like duplications in process, redundant processes, and unaccounted for systems and shelfware don’t cost the business time and money.

Vanguard enterprise architects tend to work with the long term vision in mind, and are expected to innovate to find the business new ways of reaching their future state objectives that could be more efficient than the current strategy.

It’s value to a business becomes more readily apparent when it enterprise architects operate in terms of business outcomes, and include better alignment of IT and the wider business; better strategic planning by adding transparency to the strategy, allowing the whole business to align behind, and work towards the future objective; and a healthier approach to risk, as the value (reward) in relation to the risk can be more accurately established.

Business Process

Business process solutions help leadership, operations and IT understand the complexities of their organizations in order to make better, more informed and intelligent opinions.

There are a number of factors that can influence an organization who had been making it by without a business process solution, to implement the initiative. Including strategic initiatives – like business transformation, mergers and acquisitions and business expansion; compliance & audits – such as new/changing industry regulations, government legislation and internal policies; and process improvement – enhancing financial performance, lowering operating costs and polishing the customer experience.

We can also look at the need for business process solutions from the perspective of challenges it can help overcome. Challenges including the complexities of a large organization and international workforces; confusion born of undefined and undocumented processes as well as outdated and redundant ones; competitor driven market disruption; and managing change.

Business process solutions aim to tackle these issues by allowing an organization to do the following:-

  • Establish processes where they don’t exist
  • Document processes that exist but aren’t consistently followed
  • Examine/analyze/improve/eliminate processes that don’t work
  • Optimize processes that take too long, cost too much or don’t make sense
  • Harmonize redundant processes across the organization.
  • Construct processes for new products, markets and organizations
  • Disrupt processes with new technology and data assets.

The Complete, Agile Foundation for the Data-Driven Enterprise.

As with data, these three examples of data management tools also benefit from a more fluent relationship, and for a long time, industry professionals have hoped for a more comprehensive approach. With DM, EA and BP tools that work in tandem with, and complement one another inherently.

It’s a request that makes sense too, as although all three data management tools are essential in their own right, they all influence one another.

We can look at acquiring, storing and analyzing data, then creating a strategy from that analysis’ as separate acts, or chapters. And when we bring the whole process together, under one suite, we effectively have the whole ‘Data Story’ available to us in a format we can analyze and inspect as a whole.