Author: Bunny Tharpe

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Data-Driven Business Transformation: the Data Foundation

Data foundation

In light of data’s prominence in modern business, organizations need to ensure they have a strong data foundation in place.

The ascent of data’s value has been as steep as it is staggering. In 2016, it was suggested that more data would be created in 2017 than in the previous 5000 years of humanity.

But what’s even more shocking is that the peak still not may not even be in sight.

To put its value into context, the five most valuable businesses in the world all deal in data (Alphabet/Google, Amazon, Apple, Facebook and Microsoft). It’s even overtaken oil as the world’s most valuable resource.

Yet, even with data’s value being as high as it is, there’s still a long way to go. Many businesses are still getting to grips with data storage, management and analysis.

Fortune 1000 companies, for example, could earn another $65 million in net income, with access to just 10 percent more of their data (from Data-Driven Business Transformation 2017).

We’re already witnessing the beginnings of this increased potential across various industries. Data-driven businesses such as Airbnb, Uber and Netflix are all dominating, disrupting and revolutionizing their respective sectors.

Interestingly, although they provide very different services for the consumer, the organizations themselves all identify as data companies. This simple change in perception and outlook stresses the importance of data to their business models. For them, data analysis isn’t just an arm of the business… It’s the core.

Data foundation

The dominating data-driven businesses use data to influence almost everything. How decisions are made, how processes could be improved, and where the business should focus its innovation efforts.

However, simply establishing that your business could (and should) be getting more out of data, doesn’t necessarily mean you’re ready to reap the rewards.

In fact, a pre-emptive dive into a data strategy could in fact, slow your digital transformation efforts down. Hurried software investments in response to disruption can lead to teething problems in your strategy’s adoption, and shelfware, wasting time and money.

Additionally, oversights in the strategy’s implementation will stifle the very potential effectiveness you’re hoping to benefit from.

Therefore, when deciding to bolster your data efforts, a great place to start is to consider the ‘three Vs’.

The three Vs

The three Vs of data are volume, variety and velocity. Volume references the amount of data; variety, its different sources; and velocity, the speed in which it must be processed.

When you’re ready to start focusing on the business outcomes that you hope data will provide, you can also stretch those three Vs, to five. The five Vs include the aforementioned, and also acknowledge veracity (confidence in the data’s accuracy) and value, but for now we’ll stick to three.

As discussed, the total amount of data in the world is staggering. But the total data available to any one business can be huge in its own right (depending on the extent of your data strategy).

Unsurprisingly, vast volumes of data are sourced from a vast amount of potential sources. It takes dedicated tools to be processed. Even then, the sources are often disparate, and very unlikely to offer worthwhile insight in a vacuum.

This is why it’s so important to have an assured data foundation upon which to build a data platform on.

A solid data foundation

The Any2 approach is a strategy for housing, sorting and analysing data that aims to be that very foundation on which you build your data strategy.

Shorthand for Any Data, Anywhere, Any2 can help clean up the disparate noise, and let businesses drill down on, and effectively analyze the data in order to yield more reliable and informative results.

It’s especially important today, as data sources are becoming increasingly unstructured, and so more difficult to manage.

Big data for example, can consist of click stream data, Internet of Things data, machine data and social media data. The sources need to be rationalized and correlated so they can be analyzed more effectively.

When it comes to actioning an Any2 approach, a fluid relationship between the various data initiative involved is essential. Those being, Data Modeling, Enterprise Architecture, Business Process, and Data Governance.

It also requires collaboration, both in between the aforementioned initiatives, and with the wider business to ensure everybody is working towards the same goal.

With a solid data foundation platform in place, your business can really begin to start realizing data’s potential for itself. You also ensure you’re not left behind as new disruptors enter the market, and your competition continues to evolve.

For more data advice and best practices, follow us on Twitter, and LinkedIn to stay up to date with the blog.

For a deeper dive into best practices for data, its benefits, and its applications, get the FREE whitepaper below.

Data-Driven Business Transformation

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erwin Expert Blog

Where to begin business process modeling?

Knowing where to begin business process modeling can seem impossible – you have a wealth of information spread out in front of you and no clue where to start. 

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Business process management’s role in utilizing knowledge

Business Process Management

Business process management’s role in utilizing knowledge is, in essence, about alignment, making sure you have the key pieces of knowledge from individual employees, departments and operations. This way, businesses can make better decisions with greater context, based on the full picture.

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Getting started with business process modeling: Why am I doing this?

Where to begin business process modeling

Getting started with business process modeling is better done sooner rather than later. Especially since business processes modeling is essential to a data strategy.

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Business Process Management Provides Invaluable Knowledge

‘Knowledge is power’ – a well-known phrase and one that is especially true in the business world. Statistics show that Fortune 500 companies lose $31.5 billion each year by failing to gather and share knowledge effectively. So knowing the best way to undertake every business process you have will help drive your business forward.

Continue reading “Business Process Management Provides Invaluable Knowledge”
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Don’t Fool Yourself About Data Management

data management

The early stages of adopting a data strategy often involve an ad-hoc approach to data management. Rather than invest in a suite of new tools, businesses tend to make do with what they have already, starting small and eventually formalizing the approach.

In many cases, this could be the best approach – or at least better than wasting an investment on shelfware, right?

But if your business wants to use its data effectively, a point inevitably will come when the data has outgrown the makeshift means in which it is managed.

data management

Harder to Manage and Share

Enterprise architecture (EA), for example, can start as a collection of Visio files, Excel spreadsheets and PowerPoint slides, but it’s never long before the EA starts spilling from the Office Tools and onto the desk – literally. It’s not uncommon to see EA represented as Post-it Notes haphazardly scattered across a workstation.

For a while, an enterprise architect might be able to maintain this approach. But when the information needs to be shared with the wider business to influence decisions and strategy, the lack of structure can make the findings difficult to comprehend.

This not only slows down time to markets and leads to inaccurate analysis and results, but it also undermines EA’s value in the eyes of stakeholders and decision-makers. If they can’t clearly see the business outcomes, then why bother investing more money in the discipline?

Harder to Analyze

Taking this approach also limits the potential analysis an organization can even carry out. With the Office Tools approach, even though the software all falls under the “Office” bracket, the files and systems are still disparate.

Traditionally, this was less of an issue for EA in years gone by. Foundational EA, as we refer to it today, was and is about support, rather than innovation. Businesses weren’t really actioning an EA initiative to give insight into where they could innovate, the likelihood of disruption, and how that disruption could be capitalized on.

EA was more about “legacy” IT tasks like keeping the lights on, highlighting redundant systems and processes, and trimming fat to keep costs low. In other words, it was more concerned about the current state of the business and less about what needs to be done to achieve the desired future state.

In-depth and all-inclusive analysis required to maximize the data’s potential benefits, needs to be stored in one repository.

Harder to Maintain

And what happens if your enterprise architect leaves? His/her work may be rendered useless to the business due to the lack of formality.

So don’t fool yourself about data management. Given all these consideration, you need to invest in effective data management so your business can truly capitalize on its data and the valuable insights it will provide.

Data Management - Enterprise Architecture & Data Modeling White Paper

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Data Education Month: Data-Focused Organizations Continue Their March

Data Education Month

In the modern world, data education is immensely important.

Data has become a fundamental part of how businesses operate. It’s also essential to consumers in going about their day-to-day lives.

And while organizations and consumers alike go about their business, data constantly ticks in the background, enabling the systems and processes that keep the world functioning.

Considering this, and with March marking Data Education Month, now seems the perfect time to highlight the importance of understanding data’s potential, its drawbacks and the most efficient ways to ensure its effective management.

Data education month

In 2013, the total amount of data in the world was believed to have reached 4.4 zettabytes. For context, 1 zettabyte is equivalent to around 44 trillion gigabytes, or about 152 million years of UHD 8K video format.

By 2020, analysts predict the world’s data will reach 44 zettabytes. The sudden acceleration is truly staggering, and it’s businesses driving it.

Start-ups that find new ways to exploit data can revolutionize markets almost overnight. And as the frequency in which this happens increases, more and more pre-established businesses are also putting resources behind digital innovation.

By now, businesses should be more than aware of just how important a good data management strategy is. If you’ve yet to make a data strategy a central focus of the way your business operates, then chances are, you’re being left behind – and the gap is widening quickly.

So in honor of data education month, we’ve collated some of our top educational data posts, and a few others around the Web.

Read, comment, share and celebrate #DataEducationMonth with us.

Data Education: Data Management

Managing Any Data, Anywhere with Any²

The acceleration in the amount of data is staggering, and can be overwhelming for businesses. You should apply the Any² approach to cope.

GDPR Guide: Preparing for the Changes

Businesses need to prepare for changes to General Data Protection Regulation (GDPR) legislation, and our GDPR guide is a great place to start.

Using EA, BP and DM to Build 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.

Data Education: Data Modeling

The Data Vault Method for Modeling the Data Warehouse

How the data vault method benefits businesses by improving implementation times, and enabling data warehouse automation.

Data Modeling – What the Experts Think

Three data modeling experts share their advice, opinions and best practices for data modeling and data management strategies.

Data Education: Enterprise Architecture

Data-Driven Enterprise Architecture for Better Business Outcomes

A business outcome approach to enterprise architecture can reduce times to market, improve agility, and make the value of EA more apparent.

What’s Behind a Successful Enterprise Architecture Strategy?

Best practices to adopt to increase the likelihood of enterprise architecture’s success

Data Education: Business Process

Basics of Business Process Modeling

Business process modeling helps to standardize your processes and the ways in which people communicate, as well as to improve knowledge sharing.

Where Do I Start with Business Process Modeling?

FAQ blog providing insight from top consultants into key issues impacting the business process and enterprise architecture industries.

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Basics of Business Process Modeling

Business process modeling is becoming progressively more relevant. Everyday businesses aspire to make organizational changes that will boost their firms and drive them forward.

However to make a change that will really make a difference, you need to have a clear understanding as to what your business currently does – in every area.

As companies move expand quickly, few truly understand the way things are done, and that’s where business process modeling  is vital.

It’s a concept that some aren’t familiar with, so below we’ve summed up some of the frequently asked questions to get you started.

What is business process modeling?

A business process is an activity or set of activities designed to achieve a specific goal, and your organization has thousands of them!

For example, if your company delivers goods to customers, the business process is the numerous steps and actions taken to get the items from your warehouse to the customer.

If you don’t understand what your business processes are, there is a real risk that members of your organization all do things differently – some effectively and efficiently, but others in more time-consuming ways that don’t benefit  your business.

By modeling your business processes, you can know the activities being undertaken and identify the best way to do each one.

What are the benefits of business process modeling?

Most organizations understand what they need to do to get the results that they want, at least at a basic level. But clunky processes, inefficient teams, lack of information and poor communication frequently get in the way.

Employees who spend time fighting fires, hunting for data and reacting to unnecessary roadblocks are prevented from executing your strategic objectives. This is a huge factor in lethargic time to markets and stops businesses from effectively moving forward.

The aim of business process modeling is to standardize your processes and the ways in which people communicate, as well as to improve knowledge sharing.

By doing so, you have a much better understanding of what everyone is doing, you share best practices more effectively, and can implement business changes easier.

How can erwin help with business process modeling?

Working with the chief operational officer, the operations team and others, erwin’s consultants can assist in business process modeling by documenting existing business processes, designing an improved process flow, and building a plan for moving forward with organizational change.

erwin can help you do this simply and swiftly, so you don’t miss out on opportunities for growth. Our workshop approach – with our people and tools – lets us hold up a mirror and do a quick assessment.

We can capture the models and show you a picture of your organization far faster than you can and often in a way you’ve never seen before, giving a third-party objective perspective vital to moving your business forward.

If you would like help you with your business process modeling, get in touch with us today.

Importance of Governing Data

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Why the NoSQL Database is a Necessary Step

 The NoSQL database is gaining huge traction and for good reason.

Traditionally, most organizations have leveraged relational databases to manage their data. Relational databases ensure the referential integrity, constraints, normalization and structured access for data across disparate tools, which is why they’re so widely used.

But as with any technology, evolving trends and requirements eventually push the limits of capability and suitability for emerging business use cases.

New data sources, characterized by increased volume, variety and velocity have exposed limitations in the strict relational approach to managing data.  These characteristics require a more flexible approach to the storage and provisioning of data assets that can support these new forms of data with the agility and scalability they demand.

Technology – specifically data – has changed the way organizations operate. Lower development costs are allowing start ups and smaller business to grow far quicker. In turn, this leads to less stable markets and more frequent disruptions.

As more and more organizations look to cut their own slice of the data pie, businesses are more focused on in-house development than ever.

This is where relational data modeling becomes somewhat of a stumbling block.

Rise of the NoSQL Database

More and more, application developers are turning to the NoSQL database.

The NoSQL database is a more flexible approach that enables increased agility in development teams. Data models can be evolved on the fly to account for changing application requirements.

This enables businesses to adopt an agile system to releasing new iterations and code. They’re scalable and object oriented, and can also handle large volumes of structured, semi-structured and unstructured data.

Due to the growing deployment of NoSQL and the fact that our customers need the same tools to manage them as their relational databases, erwin is excited to announce the availability of a beta program for our new erwin DM for NoSQL product.

With our new erwin DM NoSQL option, we’re the only provider to help you model, govern and manage your unstructured cloud data just like any other traditional database in your business.

  • Building new cloud-based apps running on MongoDB?
  • Migrating from a relational database to MongoDB or the reverse?
  • Want to ensure that all your data is governed by a logical enterprise model, no matter where its located?

Then erwin DM NoSQL is the right solution for you. Click here to apply for our erwin DM NoSQL/MongoDB beta program now.

And look for more info here on the power and potential of  NoSQL databases in the coming weeks.

erwin NoSQL database

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Data Vault Modeling & the Data Warehouse

Data vault

The data vault method for modeling the data warehouse was born of necessity. Data warehouse projects classically have to contend with long implementation times. This means that business requirements are more likely to change in the course of the project, jeopardizing the achievement of target implementation times and costs for the project.

To improve implementation times, Dan Linstedt introduced the Data Vault method for modeling the core warehouse. The key design principle involves separating the business key, context, and relationships in distinct tables as hub, satellite, and link.

Data vault

Data Vault modeling is currently the established standard for modeling the core data warehouse because of the many benefits it offers. These include the following:

Data Warehouse Pros & Cons

Data Warehouse Benefits

• Easy extensibility enables an agile project approach
• The models created are highly scalable
• The loading processes can be optimally parallelized because there are few synchronization points
• The models are easy to audit

But alongside the many benefits, Data Vault projects also present a number of challenges. These include, but are not limited to, the following:

Data Warehouse Drawbacks

• A vast increase in the number of data objects (tables, columns) as a result of separating the information types and enriching them with meta information for loading
• This gives rise to greater modeling effort comprising numerous unsophisticated mechanical tasks

How can these challenges be mastered using a standard data modeling tool?

The highly schematic structure of the models offers ideal prerequisites for generating models. This allows sizable parts of the modeling process to be automated, enabling Data Vault projects to be accelerated dramatically.

erwin data intelligence

Potential for Automating Data Vault

Which specific parts of the model can be automated?

The standard architecture of a data warehouse includes the following layers:

  • Source system: Operational system, such as ERP or CRM systems
  • Staging area: This is where the data is delivered from the operational systems. The structure of the data model generally corresponds to the source system, with enhancements for documenting loading.
  • Core warehouse: The data from various systems is integrated here. This layer is modeled in accordance with Data Vault and is subdivided into the raw vault and business vault areas. This involves implementing all business rules in the business vault so that only very simple transformations are used in the raw vault.
  • Data marts: The structure of the data marts is based on the analysis requirements and is modeled as a star schema.

Standard Architecture of a Data Vault

Both the staging area and the raw vault are very well suited for automation, as clearly defined derivation rules can be established from the preceding layer.

Should automation be implemented using a standard modeling tool or using a specialized data warehouse automation tool?

Automation potential can generally be leveraged using special automation tools.

What are the arguments in favor of using a standard tool such as the erwin Data Modeler?

Using a standard modeling tool offers many benefits:

  • The erwin Data Modeler generally already includes models (for example, source system), which can continue to be used
  • The modeling functions are highly sophisticated – for example, for comparing models and for standardization within models
  • A wide range of databases are supported as standard
  • A large number of interfaces are available for importing models from other tools
  • Often the tool has already been used to model source systems or other warehouses
  • The model range can be used to model the entire enterprise architecture, not only the
    data warehouse (erwin Web Portal)
  • Business glossaries enable (existing) semantic information to be integrated

So far so good. But can the erwin Data Modeler generate models?

A special add-in for the erwin Data Modeler has been developed specifically to meet this requirement: MODGEN. This enables the potential for automation in erwin to be exploited to the full.

It integrates seamlessly into the erwin user interface and, in terms of operation, is heavily based on comparing models (complete compare).

MODGEN functionalities

The following specific functionalities are implemented in MODGEN:

  • Generation of staging and raw vault models based on the model of the preceding layer
  • Generation is controlled by enriching the particular preceding model with meta-information, which is stored in UDPs
  • Individual objects can be excluded from the generation process permanently or
    interactively
  • Specifications for meta-columns can be integrated very easily using templates

To support a modeling process that can be repeated multiple times, during which iterative models are created or enhanced, it is essential that generation be round-trip capable.

To achieve this, the generation always performs a comparison between the source and target models and indicates any differences. These can be selected by the user and copied during generation.

The generation not only takes all the tables and columns into consideration as a matter of course (horizontal modeling), it also creates vertical model information.

This means the relationship of every generated target column to its source column as data source is documented. Source-to-target mappings can therefore be generated very easily using the model.

Integrating the source and target model into a web portal automatically makes the full impact and lineage analysis functionality available.

If you are interested in finding out more, or if you would like to experience MODGEN live, please contact our partner heureka.

Data Modeling Data Goverance

Author details: Stefan Kausch, heureka e-Business GmbH
Stefan Kausch is the CEO and founder of heureka e-Business GmbH, a company focused on IT consultancy and software development.

Stefan has more than 15 years’ experience as a consultant, trainer, and educator and has developed and delivered data modeling processes and data governance initiatives for many different companies.

He has successfully executed many projects for customers, primarily developing application systems, data warehouse automation solutions and ETL processes. Stefan Kausch has in-depth knowledge of application development based on data models.

Contact:
Stefan Kausch
heureka e-Business GmbH
Untere Burghalde 69
71229 Leonberg

Tel.: 0049 7152 939310
Email: heureka@heureka.com
Web: www.heureka.com