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Essentials of Data Governance

pexels-christina-morillo-1181341-1030x688 Essentials of Data Governance

In the era of emerging technologies, data has become essential for organizations. With rapid digital transformation across industries, gaining a competitive advantage is crucial for thriving in the market. Today, data is the new “oil” that forms an organization’s core for business growth. However, the rate of data generation has become enormous. A recent report by Towards Data Science produced the statistics of data generation that stands at a whopping  2.5 quintillion bytes. Additionally, the current projections state the data generation rate to rise to 133 zettabytes by 2025.

In recent years, the increase in the number of data breach cases has doubled. The imminent threat in a business is the possibility of data breaches. To bolster data protection, it is of utmost importance to have a robust data governance framework. As per IBM data breach reports, the average cost of a data breach is highlighted as $3.86 million, while the USA alone recorded a breach of $8.64 million.

There is a need for robust data governance framework to tackle such challenges. Standard data governance ensures data security, data quality, and integrity while providing the traceability of the data origins. Also, data governance can be successfully implemented when high-quality data is readily available with crucial information on the data types, which is achievable with a data catalog.  Besides, an organization attains firm control over its data usage policies when a regulatory body imposes stricter guidelines. Today, it is possible with some of the robust regulatory bodies available that put a strong emphasis on data governance. Among them, the most well-known is the General Data Protection Regulation (GDPR). Furthermore, a data governance approach can reach its ultimate goal within an enterprise with its essential components, namely processes, policies, access controls, and data protection, encompassing the entire data-related workflow within an organization. Tech giants such as Microsoft have contributed significantly to the data governance requirements with the Azure Purview offering that has reach achieved wide acceptance in the industry.

The article delves into the topic to provide a deep insight into data governance and its regulations.

Data Governance Overview

Data governance is a strategy that incorporates the practices, processes, and technical requirements of an organization into a framework by which an organization can achieve standardization in its workflow, thereby providing protection and the appropriate management of its data assets. A useful data governance model’s scalability is a must as it ensures that all the policies, processes, and use-cases are applied accurately for transforming a business into a data-driven enterprise.

Another crucial aspect of data governance is for an organization to conduct a risk assessment and compliance. The successful integration of data governance is determined by efficient data management and data security factors within the framework. An ideal governance policy must address the critical components of data storage,  the original source, and a well-defined data access strategy. Furthermore, data governance solutions focus on providing response plans relating to misuse of data and unauthorized access.

Data governance and data management are often used synonymously, but it is essential to understand that data governance forms a significant part of a data management model.

Data Catalog

A data catalog acts as the inventory of the critical data assets in an organization. The use of metadata helps to manage the data more efficiently. The data professionals benefit from a data catalog as it helps in data collection, organizing data, easier accessibility to data, and improvement of the metadata to support data discovery and governance. While the data generated is enormous in a day to day functioning of an organization, finding relevant data becomes challenging for specific tasks. Additionally, data accessibility is demanding due to various legal regulations of the organization and a particular country’s government. The key factors to understand are the data movement within an organization, such as the individuals who will have access to it and the purpose they want to access it. Such tracking of the data ensures the protection of the data as it limits unauthorized personnel. Thus a data catalog plays a crucial role in addressing some of the challenges related to data.

  • A data catalog provides all the essential data required by an organization; therefore, data accessibility from a single point ensures reduced time for searching data.
  • Creating a business vocabulary.
  • Efficient transformation of data lakes into data swamps.
  • Identifying the different structures of the data.
  • Availability of high-quality and reliable data.
  • Data reusability possibilities

An organization can achieve a competitive advantage with the appropriate use of data. Therefore the data should be trustworthy from the appropriate sources. Some of the organizations’ key members, such as C-level executives, use data for business decisions. Thus, a data catalog becomes useful for looking at cost-saving and operational efficiency factors with a keen eye on fraud and risk analysis.

Data Governance Framework

A data governance framework allows an organization to focus on achieving the business goals and data management challenges while providing the right means to attain them more speedily and securely. Besides, the results of a data governance integration are scalable and measurable.Key-Participants-in-a-Data-Governance-Framework Essentials of Data Governance

Figure. Key Participants in a Data Governance Framework. Source

 

Some of the essentials of a data governance framework are:

  • Use Cases

The data governance framework must address some critical factors, such as the use case for several business scenarios in an organization. The data governance use cases should interlink the need for a data governance framework and its contribution to achieving business goals. Ideally, the use cases are derived from significant factors in an organization, such as revenue, cost, and the associated risks. The category-related use case addresses the enrichment of products and services, innovations, market opportunities, and the ability to achieve them at a reduced cost of maintenance with efficiency, auditing, and data protection.

  • Quantification

The need to quantify data is an absolute necessity as it produces data governance integration in the organization. A business needs to ascertain that they are following, covering all the categorized use cases with evidence to monitor the performance and provide future insights.

  • Technical Benefits

With the technical addition in a workflow, the data governance solutions can efficiently address some of the critical components, thereby ensuring efficiency. The data governance must address factors like the need for technology investment and the primary members who will work with data-related processes. A technical infusion in the workflow also enables the easier discoverability of data definitions, data categories, data lineage, and the appropriate classification of data as trustable data or untrustworthy data. The technical addition also makes it possible to create a feedback mechanism for resolving regulatory issues and policies concerning data usage.

  • Scalability

The data governance policies should be capable of providing scalable results. Using a scalable model provides growth opportunities for an organization by addressing the problems in a data lifecycle. The primary focus is to introduce new tools to reduce operational costs and provide data protection for business growth.

Data Governance Processes

The data government processes comprise of the following.

  • The organization must be mindful of the essential documents such as regulatory guidelines, statutes, company policies, and strategies.
  • A clearly defined workflow states legal mandates, policies, and objectives to be synchronized to help an organization meet data governance and management compliance.
  • Data metrics to be incorporated to measure the performance and the quality of the data.
  • Principles of data governance to be met.
  • Identification of the data security and privacy threats.
  • Control measures to ensure smoother data flow with a precise analysis of the risks.

Data Governance Policies

Under data governance, there are various policies to determine the effectiveness of the organization’s operational strategies. Some of the policies related to data accessibility, data usage, and data integrity are incredibly crucial for successful data governance implementation. The most important policies that an organization must follow for successful data management are as follows.

  • Data Structure policy
  • Data Access Policy
  • Data Usage Policy
  • Data Integration Policy

 Privacy and Compliance Requisites

The organizations are associated with a significant amount of highly sensitive data. Therefore, an organization needs to follow the regulatory compliance of data governance. In the context of business, privacy refers to an individuals’ right to have control over the type of personal data they want to be collected and used and the sensitive information that should be restricted. As per EU directives for data governance, sensitive data is defined as the data that contains a name, address, telephone number, and email address of an individual. On the other hand, sensitive personal data is distinguished clearly, as the data contains information on a person’s ethnicity, political opinion, religion, race, health-based information, criminal conviction, and trade union-based membership details. Such data have stricter guidelines that must be followed with due diligence.

Role of General Data Protection Regulation (GDPR)

The General Data Protection Regulation (GDPR)  was established in the year 2016. The primary aim of the regulation was to provide a framework for data privacy standards. GDPR states that any company looking to conduct business in Europe must be willing to adhere to data protection norms. The GDPR has strict guidelines that ensure the protection and privacy of personal data for its citizens. The mandate was an update from the previous Data Protection Directive in Europe.

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Figure. Crucial Requirements of GDPR. Source

 

Under GDPR, the mandate’s scope extends its reach in terms of the territorial horizon while providing a well-defined law for processing personal data by offering their business services in Europe. The organizations or individuals aiming to provide their services without the presence in Europe are monitored for their service offering under GDPR guidelines. The tracking of such services includes online businesses that require users to accept cookies to access their services. GDPR also differentiates the various data types and the data considered personal data under the mandate.

Furthermore, the direct and indirect data are interlinked with the identification of data subjects. The data subjects are people who can be identified with their information presented in the data. The data in this context is related to personal information such as names, addresses, IP addresses, biometric data logs, citizenship-based identification, email, and the profession.

Additionally, the GPPR mandate ensures that the data is collected within the limits of the law, and it should be highly secured while it exists the records of the organization with stricter rules for its uses. The primary categories of GDPR data governance requirements are:

  • There must be a classification of personal data, while personal identification data must have limited usability. The individuals can access their data and hold the right to request personal data removal or rectification. The mandate also states mandatory data processing requirements and portability of data.
  • Data protection is a must, and it should cover all aspects of safeguarding personal data collected. Also, there must be confidentiality, integrity, and availability of the data collected for business purposes. The organizations should also adhere to data restoration regulations for scenarios that may involve data loss due to technical failure or accidents.
  • The collected data must be well- documented as per legal procedures.

Access Controls

Access controls form an integral part of access governance that regulates the accessibility of data. The critical areas covered comprise the guidelines to specify who can access the data and view it. Additionally, it specifies that there is a requirement to state the purpose of data access in the organization. The compliance of access controls allows eliminating unauthorized access of data.

As per the GDPR mandate, some of the data protection requirements must enforce specific procedures.

  • There must be accountability associated with data protection requirements. Data protection personnel must be appointed to manage data and monitor its activities for organizations involved in data processing activities. The appointed individuals must ensure that the data protection standards are met.
  • Data storage is the essential factor for data privacy. Therefore, organizations must have a data map and data inventory to track the source of data and its storage. The source includes the system from which it was generated while tracking the data lineage to provide comprehensive data protection.
  • Data accuracy is paramount, and organizations must keep up-to-date data to achieve high-quality data. Also, data quality reporting must be followed to keep up with data quality standards.

Data Protection

  • Data intelligence provisions for getting insights with 360 visibility of data.
  • Identifying data remedies for security and privacy issues.
  • To protect sensitive data with access governance and ensure no overexposed data exists with data governance methods.
  • Integrating artificial intelligence capabilities to identify dark data and its relationship.
  • Assigning labels with automation to provide data protection during the workflow and the lifecycle of the data.
  • Rapid data breach notification and its investigation.
  • Automate procedure for classifying sensitive and personal data.
  • Automated compliance and policy checks.
  • In-depth assessment of risk scores with metrics depending on the data type, location, and access consent.

Reimagining Data Governance with Microsoft Azure Purview

Azure Purview is a unified data governance service by Microsoft. The governance service enables management and governing of on-premise, multi-cloud, and software-as-a-service (SaaS) data. The users can have access to a holistic and up-to-date map of the data with automated data discovery. Besides, the classification of sensitive data is more manageable along with end-to-end data lineage. With Azure Purview, the data consumers are assured of valuable and trustworthy data.  Some of the key features of Azure Purview are discussed in the following section.

  • Unified mapping of data

The Purview data map feature establishes the foundation of practical data usage while following the data governance standards. With Purview, it is possible to automate the management of metadata from hybrid sources. The consumer can take advantage of data classification with built-in classifiers that can Microsoft Protection sensitivity labels. Finally, all the data can be easily integrated using Apache Atlas API.

unified-data-mapping Essentials of Data Governance

Figure. Unified Data Mapping using Azure Purview. Source

 

  • Trusted Data

Purview offers a data catalog feature that can allow the easier search of data using technical terms from the data vocabulary. The data can be easily identified as per the sensitivity level of the data.

  • Business Insights

The data supply chain can be interpreted conveniently from raw data to gain business insights. Purview offers the option to scan the power BI environment and the analytical workspace automatically. Besides, all the assets can be discovered with their lineage to the Purview data map.

  • Maximizing Business Value

The SQL server data is more discoverable with a unified data governance service. It is possible to connect the SQL server with a Purview data map to achieve automated scanning and data classification.

  • Purview Data Catalog

The Purview data catalog provides importing the existing data dictionaries, providing a business-grade glossary of terms that makes data discoverable more efficiently.

Conclusion

Business enterprises are generating a staggering amount of data daily. The appropriate use of data can be an asset for gaining business value in an organization. Therefore, organizations need to obtain reliable data that can provide meaningful business insights. Advanced technologies such as artificial intelligence and data analytics provide an effective way of integrating data governance in the operational workflow. Today, tech giants like Microsoft, with their data governance offering: Azure Purview, have paved the way for other organizations to opt for data governance. Many startups follow in the footsteps and have acknowledged the importance of data governance for high-quality data while ensuring data privacy at all times, thereby offering several data governance solutions in the market. A robust data governance framework is essential for maintaining the data integrity of the business and its customers.

 

 

Integrate Data Silos with Azure Synapse Analytics

The Roadblock for Digital Transformation

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Source: Harvard Business Review

It is clearly established that Digital Transformation is the key to success and even survival for organizations, even more so with the current global crisis due to COVID-19. 64% of executives believe that they have less than four years to complete digital transformation or they will go out of business. 91% of global executives surveyed by Harvard Business Review feel that effective data and analytics strategies are essential for digital transformation. This data driven culture is critical to spark innovation and drive efficiencies, which is crucial for survival.

But, 80% of the respondents also say that their organizations are struggling to become mature users of data and analytics even though 79% of the employees use data and analytics at least once a week. What gets in the way of organizations effectively using data and analytics for business decisions?

More than half (55%) of the executives say the key roadblock stems from data silos and difficulty managing data coming from multiple systems. Digital transformation leads to a lot of data being captured across various systems which can be extremely valuable. However, less than 20% of this data can ever be analyzed due to the silos. This is mainly because of the disconnect between Big Data analytics, enterprise Data Warehousing, Analytics, and Artificial Intelligence/Machine Learning.

Simplifying Analytics

The need of the hour is to simplify analytics in a manner that breaks down these silos and makes the most of the data available for analysis without having to jump through hoops. In an ideal world, streaming operational data should be available for immediate analysis to generate reports and run models on the data. This is not a trivial problem.

Operational data is a mix of structured and unstructured data which is generally stored in a Data Lake, not suitable for analytics. Hence, the operational data needs to be imported into a Data Warehouse. The reporting and analytics services can then run on the Data Warehouse.

This creates three key issues. 

  1. Lag between the operational and analytics data stores due to the ELT pipeline. 
  2. Balancing the operational, ELT, reporting, and analytics workloads in the cloud. 
  3. Efficient and effective model management.

Organizations would really benefit from a framework which effectively addresses these issues and removes the roadblocks to data maturity. Azure Synapse Analytics is a step in the right direction with a big promise – Limitless Analytics Services in the cloud.

Azure Synapse Analytics to the Rescue

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Source: Microsoft

Microsoft has launched Azure Synapse Analytics to fulfill the promise of limitless analytics services. This service creates a single place to collaborate for Data Engineers, Database Administrators, Data Scientists, Business Intelligence Analysts, and Business Users with everyone accessing the same data.

The service offers a distributed query processing engine, versatile form factor for computing (cluster/ serverless), and a single experience for the users to manage the end-to-end process. This provides the much required flexibility in scaling and a great user experience, which promotes collaboration.

Many features of Azure Synapse Analytics are now generally available with many more in the pipeline. We believe that this service will evolve rapidly into the standard for analytics at scale for organizations.

Benefits of Azure Synapse Analytics

Azure Synapse Analytics allows teams to seamlessly work together. However, the benefits go beyond this. Some additional benefits are:

1. Unified Experience

 

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Source: Microsoft

Azure Synapse Analytics allows users to ingest, prepare, manage, serve, visualize, and analyse the data using a unified experience. Users can bring their analytics to where the data is located, rather than switching to a different interface. This gives a big boost to productivity.

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2. Limitless Scale

Azure Synapse Analytics enables limitless scaling for data and analytics in the cloud. Data professionals can derive insights from all the data across data warehouses and big analytics systems at speed. They can query both relational and non-relational data at petabyte-scale using T-SQL language. Furthermore, they can benefit from a versatile form factor of using clusters and serverless computing. Finally, they can run analytics systems along with mission critical workloads with intelligent workload management, workload isolation, and limitless concurrency.

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3. Integrate Business Intelligence and Machine Learning

Azure Synapse Analytics allows users to integrate Power BI and Azure Machine Learning within the Azure Synapse Studio. Then BI professionals and Data Scientists can tap into the available data immediately to create faster insights.

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4. Cloud-Native HTAP Implementation

The announcement of Azure Synapse Link (Preview) brings cloud-native hybrid transactional and analytical processing (HTAP) for Azure Cosmos DB. And with this, plans to expand it to other data stores in the future. It creates a tight seamless integration between Azure Cosmos DB and Azure Synapse Analytics. This enables users to run near real-time analytics over operational data which is stored in Azure Cosmos DB.

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Want to learn more? Click here for a quick and informative video that demonstrates the power of Synapse Analytics Link.

5. Price-Performance

Price-performance is also a critical part of data solutions. According to Microsoft, Azure Synapse Analytics offers better price-performance as compared to Google BigQuery and Amazon Redshift based on field tests done by GigaOm.

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Source: GigaOm Report

The TPC-H and TPC-DS results published by Microsoft show a significant reduction in price of Azure Synapse Analytics as compared to the others in the preceding as well as following graphics.

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Source: Microsoft

Speed

As demonstrated in this video from Ignite 2019, Azure Synapse Analytics can be blazing fast in a petabyte-scale environment combining relational and non-relational data. This can be a game changer for organizations where faster decision making can lead to substantial profit increase.

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Getting Started

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We have multiple offers that make it easy for organizations to get started with Azure Synapse Analytics no matter what stage they are at in the process. 

  1. Just getting started?  We offer a free two-hour lunch and learn workshop to help you understand this service. 
  2. Do you already know about the service but need help figuring out your next step?  We can conduct an assessment, strategy, and roadmap workshop that will provide your organization a plan with how to move forward. 
  3. Do you have a roadmap but need help with implementation? We can get you started with the first pilot which can be completed in 2-4 weeks. Once you have experienced the value from the pilot, we can help you with the implementation as per the roadmap.

 

Contact us at info@optimusinfo.com to get started.

Digital Collaboration: Superhighway to Innovation and Economic Transformation

Federal and provincial governments in Canada along with the private sector are undertaking a major initiative to establish the nation as a global centre for digital transformation and innovation, generating 50 thousand jobs and boosting GDP by some 15 billion dollars over the next 10 years. 1.4 billion dollars is being committed in a collaboration of large and small companies, working together to generate solutions to some of the world’s most pressing health, productivity and sustainability issues.

Given the media attention being paid to technology and the Internet of Things today, it would be easy to assume that digital transformation throughout the Canadian economy – and elsewhere in the world – was happening quickly. But that’s not always the case.

Much of this digital transformation, of course, is taking place in the private sector, so one reason that the pace of change may be slower than we would assume is because the money to make it happen is coming from the private sector, itself. In other words, if and when corporate funds are available – and they’re not siphoned off to other projects – then investment in digital technologies can occur.

Another fundamental roadblock to speedier adoption of digital technology is a lack of leadership within a company[i]; there is often no single individual clearly in charge of leading such a project. Frequently, this is because corporate management lacks a firm knowledge and grasp of what is at stake.

Maintaining Software Integrity in Mission Critical Application

Optimus has developed rigorous standards for software testing along with a proprietary tool – the Azure Test Harness – which ensures integrity and faster time-to-market for companies rolling out applications.

For one client, a Finnish firm that provides detailed, real-time data on severe weather, like lightning strikes, for the airline industry, the ability to roll-out error-free software on a regular basis is critical.

The company frequently updated its software which meant manual testing to ensure the application remained error-free – a very time-consuming operation especially with airlines needing the latest data, instantly.

Optimus deployed an automated test framework on the Azure Test Harness which now allows the company to reduce its test cycles significantly while expanding its test coverage and maintaining the high integrity of its web-based app.

Azure Test Harness is one example of the innovative thinking Optimus is bringing to market.

It boils down to one simple fact: a failure to realize the powerful business advantages that digital technology brings to the table.

Digital Transformation Won’t Wait

International Data Corporation (IDC), a global research firm that provides marketing intelligence to technology markets, says that in 2018, only 30% of manufacturers investing in digital transformation will be able to maximize their advantages; the rest are held back by outdated business models and technology[ii].

There is one point on which even the late adapters can agree: digital transformation won’t wait.

In its 2017 budget, the federal government of Canada put forward a bold plan to supercharge innovation in the Canadian economy. The government earmarked nearly one billion dollars to be supported by money from the private sector for a combined total of 1.4 billion dollars[iii]. The money is being utilized across five “superclusters”, one of which is the digital technology supercluster.

At the Forefront of Innovation and Made in Canada

This cluster of companies, large and small – even start-ups – hails from healthcare, mining, financial services, telecom and other sectors of the economy. There are more than 270 companies that are part of the supercluster and they’ll collaborate on more than 100 projects, literally sharing talent and ideas. The goal is to generate commercial opportunities and economic prosperity that will keep Canada at the forefront of global innovation.

Optimus Information will be integrally involved, through our longstanding partnership with Microsoft, in assisting independent software vendors that are part of the digital technology supercluster as well as the large enterprise organizations that are participating. Many of the leading companies in these areas are already our clients. Optimus will offer expertise in technologies like blockchain, one of our growing areas of strength, through applications, another Optimus area of expertise.

What is exciting about this digital supercluster project is that the participants are operating at the edge of innovation, exploring and developing what’s never been done before. Optimus intends to leverage powerful AI and data analytics capabilities with which we work on a daily basis.

Optimus has breadth and depth of talent and experience in full stack and mobile development, software testing and business intelligence, data analytics and machine learning for industries from aerospace to transportation.

Contact us today to learn how we can bring value to your company and help you stay on the leading edge of innovation, as well.

Think Big: How Design Plus Data Will Change Your Business

Is design thinking catching your attention? It should. Data insights not available before now can transform your business models and allow you to lead in your industry when you incorporate elements such as predictive, mobile dashboards and machine learning. This wave of change is forcing data architects to re-think and re-design how programs and applications must be built. To truly innovate, design teams need to push the design thinking envelope on almost every project.

“You can have data without information, but you cannot have information without data.”
– Daniel Keys Moran, computer programmer and science fiction writer.

Since the invention of the first computer, the world has been on a digital light-speed journey – one that has seen massive change in how we interact with our world and with each other. Today, there are more than 2.5 billion[i] smart phones carried in people’s pockets – each more powerful than the ones used to run the spacecraft that landed the first men on the Moon.[ii] In particular, how we interact with and gain insight from data has gone through an incredible transformation. We have evolved from relying on simple historical reporting – from the days of simple reporting to now, where tanker.

The Way It Was

Reporting has always been a critical element for a business to thrive and we have been accustomed to seeing our reports – our data – in fairly standard and historic terms. Let’s take a straightforward quarterly sales report at a consumer retail company, for example. Simple data, like units sold, prices received, cost of goods, volume of shipments and so forth, would be gathered and stored over a three-month period and then used to generate a few charts and graphs. Conclusions would be drawn from this static data and the company would shift strategy based on the conclusions.

Perhaps the conclusions were accurate and maybe they weren’t. Regardless, that’s how it’s been done for a long time: based on the data available.

The Way It Is

Today, the capability exists to break down data into far greater detail, do it in real-time and through disciplines like machine learning and artificial intelligence, draw highly focused and accurate conclusions not at the end of a business quarter but at the end of each day, and, in many cases, as it happens.

IoT Changes Shipping Industry – Reduces Risk and Cost

A client that operates a fleet of tankers equipped with IoT sensors wanted to move beyond its basic data reports and drill deeper into the technical data gathered aboard its vessels. Optimus utilized elements from Microsoft’s IoT Suite, including Azure Data Factory, to create visually appealing reports and dashboards that contained information gathered from thousands of sensors throughout the fleet.

The results meant a far more in-depth data analysis than the company had been getting, delivering more accurate insight for more accurate business decisions. When it comes to tankers, a simple mistake can cost millions in terms of lost time, environmental disasters, financial penalties, missed deadlines and more.

Optimus solved the client’s existing problem while building a platform for continuous improvement with data analysis using Microsoft Azure tools. Because the data can be aggregated in the cloud, the client can analyze greater amounts of data over an extended period of time, thus further enhancing their shipboard operational analysis and implementing global cost saving efforts as a result.

Now, a business can make highly informed decisions immediately and adjust accordingly. Of course, it’s not simply analyzing a few traditional data points, like sales; it’s analyzing where those sales took place, in which store locations, even in which aisles or departments, at what time of day, from which shelf the customer chose a purchase, what the customer’s likely income level is– in other words, the more highly specialized the data, the more highly specialized and precise the conclusions that can be drawn.

Because it’s possible to generate highly detailed data and analyze it from so many different perspectives, every sector of the economy is making use of data analysis.

In the manufacturing sector, factory operations are being revolutionized[iii] by both big data and analytics. Sensors generate endless streams of data on the health of production line equipment, data that’s being examined by the minute for the slightest indication of a potential problem or defect. Conclusions are drawn and actions implemented immediately to avoid any breakdown and disruption in the production process. There’s a positive ripple effect to this: customers don’t experience delays and the company doesn’t experience a loss of revenue.

The virtually unlimited storage capacity in the cloud, coupled to highly sophisticated computer algorithms that can perform serious analysis in, literally, seconds, is placing tremendous demands on data architects. Programs and applications must be agile enough to allow for updates, added features and improvements without delay. This has meant developing new architecture that can not only run a program at lightning speed but can be altered or updated in the areas where it needs improvement, much like making incremental improvements to a car model but without re-designing the whole car every time.

Gone are the days of a monolithic software structure where data warehouses needed a year or more to be designed and several more months for data to be inputted. If missing data was discovered, it would mean an entire rebuilding of the program.

Microservices and Teams

Today, Optimus Information designs architecture so that updates, changes or improvements can be made to one area of a program or application without having to open up the whole program. By using microservices in our software development, Optimus has created functional teams whose responsibility is to just one area of a program. A team focuses only on its specific area and generates improvements without impacting other teams or resulting in an overhaul of an entire software product. Tremendous amounts of time are saved for our clients and the cost of updates or re-designs is driven down dramatically.

Optimus applies the same method to data gathering. By means of advanced tooling, our clients can store raw data, without pre-aggregating it, run a query on that raw data and have the answers they need in a matter of seconds. Previously, it would take weeks to get a result because the data would have to be assessed and compartmentalized as it was gathered and placed into structured environments before a query could be run. This is what we call modern data warehousing. The focus is on agility and speed.

Down the Road from Microsoft by Design

Optimus specializes in working with IT departments of companies that don’t or can’t spend the time and money to develop the cloud-based software architecture needed today. Optimus uses a suite of leading edge services, on the Microsoft Azure platform, that allow us to select exactly the right components to solve a client’s problem. We are physically located close to Microsoft’s Vancouver and Redmond development centres

Optimus is a Microsoft Gold Partner and, in that role, we work very closely with Microsoft on new product previews and trials that are in development, giving feedback that improves our customer’s end product. Optimus employees have often already kicked the tires on new Azure features before they are released. This keeps us at the forefront of rapidly changing technology but let’s us give feedback as enhancements are designed.

If you want to enhance and sharpen the results of your data analysis, we invite you to contact us. We are happy to explore some “what-if” scenarios with you to help propel your data insights – and your business – forward exponentially. Reach out and schedule a virtual coffee anytime.

Game Changers: The Role of Big Data in the Future of Credit Unions

In 2002, Billy Beane was the manager of the Oakland Athletics in Major League Baseball. Oakland was a small market club with a similar sized budget and it struggled to be competitive.

Because Oakland didn’t have the money of big market teams like the New York Yankees or Los Angeles Dodgers, Beane knew he couldn’t hope to attract the high-priced talent – the superstars – to play in Oakland.

Enter Paul Depodesta, aged 27, an economics graduate from Harvard, with an analytical mind and a love of baseball. His arrival on the doorstep of the Oakland A’s gave birth to data analysis in professional sports.

He analyzed player stats, using computer algorithms, and his results allowed Oakland to sign inexpensive players that other teams dismissed. The A’s were propelled into the stratosphere of success, thanks to big data.

The A’s finished the 2002 season with 103 wins, the same number as the New York Yankees – but with a budget about a tenth the size.

This is the “secret sauce” in data analytics: the ability to take substantial amounts of information – in the case of Oakland, endless baseball player statistics – look for patterns and capitalize on what is found.

Credit Unions, Machine Learning and Data Analytics

Credit unions in Canada are rapidly embarking on the same exploration. Using machine learning and data analytics, these financial firms are finding ways to improve service to their clients while, at the same time, discovering nuggets of information from the vast amounts of data they collect, that can then be turned into business opportunities.

Virtually every customer transaction within a credit union is electronic, and the amounts of data being collected are staggering. The need to analyze this information is what drives credit unions today to embrace machine learning and data analytics.

Matthew Maguire is the Chief Data Officer at Co-Op Financial Services, a California-based company that operates an interlinked system of ATM machines throughout the U.S. and Canada. He argues that machine learning and data analysis are critical for mid-sized credit unions as they work to reinforce current customer relationships and build new ones.

“Data is coming in from different places and the challenge is… how do you make it all connect?[i]” he said.

Credit unions are moving quickly into data analysis. Through machine learning, which unearths customer transaction patterns by using algorithms, credit unions are learning a great deal about their customers and are designing strategies to capitalize on that in order to drive sales.

But, for credit unions, data enables other capabilities. Patterns of fraud can be easier to spot and shut down through data analysis.

When a client invests with a credit union, regulations require the client to complete what’s called a Know Your Client form, which essentially draws a profile of risk tolerance and investment objectives. If the client’s portfolio strays from that profile and becomes riskier, big data can alert the financial institution and the problem can be corrected before any monetary loss accrues to the client – or to hundreds of thousands of clients.

Chris Catliff is the president and CEO of Blueshore Financial, a B.C.-based credit union with more than $3 billion in assets. His vision of the future of credit unions is predicated on the power of data analytics in combination with machine learning.

He envisions the day very soon when a client approaching a branch receives a text message saying the client is already checked in at the branch. As they walk through the door, their customer profile and picture pop up on a screen [ii] at a concierge desk and they’re greeted by name.

Blueshore’s ATM machines will respond to a customer’s biometrics and offer a transaction based on a pattern of previous transactions. Up-sell opportunities will present themselves, so staff can suggest options – situations that might never occur without data analysis.

Service, he said, “has to be electronic transactions with the introduction of superior, human touch at various critical points. It’s high tech and high touch.”

Explore Your Data Potential

Like the members they serve, every credit union is unique. It is imperative for a credit union to work with data specialists who can marry the individual needs of each credit union with high levels of expertise across big data, data analysis and machine learning.

One of our strengths here at Optimus is our track-record in the areas of data gathering, analysis, machine learning, dashboarding and data visualization, through which we help our clients tailor data mining and analysis to their business goals.

At the end of the day, it’s all about staying competitive and, like the Oakland Athletics, reaching the pinnacle of success by embracing and employing new strategies to achieve that success.

 

[i] https://www.pymnts.com/big-data/2018/credit-unions-big-data-authentication-aml-kyc/
[ii] http://enterprise-magazine.com/features/betting-big-on-big-data/