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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.

Crucial-Requirements-of-GDPR- Essentials of Data Governance

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.

 

 

The Greenest Cloud: Looking at Microsoft’s New Green Plan

Microsoft-Green-Plan-300x169 The Greenest Cloud: Looking at Microsoft’s New Green Plan

Microsoft’s Project Natick at the launch site in the city of Stromness. (Photography by Scott Eklund/Red Box Pictures)

With the material world being geared increasingly towards understanding climate change and carbon footprints, we often forget that the digital world contributes to fossil fuel emissions as well. Looking at the trends of today, we can predict that digital consumption will only increase over time, therefore adding to the carbon footprint of these companies. How are different cloud providers dealing with this issue? Let’s look at Microsoft. 

Microsoft, one of the Big Three cloud providers, has brought 2 plans to the climate change table: one to reduce the company’s carbon footprint to allow for a “greener” cloud, and the other to implement underwater data centers powered by renewable energy. Seems like a lot to process? Let’s break it down. 

Microsoft’s new “Green Plan” outlines how their data centres will run on 60% renewable energy by 2020. At the end of 2018, 50% of the company’s energy was renewable and carbon neutral. Wanting to cut their carbon emissions by 75% by the end of 2030, the company has implemented an internal “carbon tax” for business divisions. Taking tangible steps to reduce the effect of the buildings and resources used in the past, they are also redesigning their campuses into carbon neutral havens. Although Apple, Google, Amazon and the likes are all rapidly moving in the same direction, a great victory for companies of such large proportions, Microsoft will be the first company to reach a campus of zero-carbon and zero-waste goals.

What Does This Mean for the Future?

It doesn’t mean being satisfied with what they have done so far. Being at the forefront of technological innovation, one of the most remarkable stories of the digital age proves to be Microsoft’s “underwater data centers”.

natickfrance063-768x512-1-e1584646711233-300x171 The Greenest Cloud: Looking at Microsoft’s New Green Plan

Image by Frank Betermin/Naval Group

 

In the testing stages now, these centers are quick to deploy, and could provide internet connectivity for many years to come. Imagine a 40 foot long data pod, sleekly dropped into the depths of the ocean off the Coast of Scotland. Seems like something out of your sci-fi novel dreams? We agree. Not only is the concept of this data center a force to be reckoned with, but the implementation of this idea could also allow for ways of delivering AI applications and gaming content to end users. Operating without maintenance for 5 years, the pod’s cooling system uses ocean water, sustainably managing itself. 

Microsoft CEO, Satya Nadella, says “Since 50 percent of the world’s population lives close to water bodies, we think this is the way we want to think about future data center regions and expansion”. Data centers are now the backbone of the world. Rather than keeping them in tightly stored boxes lining facilities, Microsoft wants to integrate them into the natural world, and use this sustainable energy to help them get there. Using submarine technology and applying this research, Microsoft is tangibly changing the way that we look at storing data. The whole process of creating one of these underwater data centers took a total of 90 days as compared to the usual 2 years of production time. This would allow the company to react to demand without facing shortages. The Azure Cloud platform is becoming the computer of the world, and the world is Microsoft’s literal oyster.

 

Starting a Data Project

chart-close-up-data-desk-590022-e1583771218589-300x168 Starting a Data Project

It’s exciting to hear ‘Data is the new Oil’ or the ‘new Gold’ or the new ‘something valuable’. What I dread, though, is the day we hear ‘Data is the new fad and a complete waste of money’. I hope that day never comes!

A lot will depend on how businesses approach data projects. Right now, it could go either way. There are many organizations throwing money at data projects to ensure they are not left behind. There are many more who are not even getting started fearing the outcome or the futility of it. If you belong to either camp, I will share a simple process to maximize the return on your data projects.

Where Data Projects fail

Data projects are complex and resource intensive and hence have many failure points. Most failure points are like the failure points of any complex project. Data availability, data quality, team quality, team work, communication, and so on. There is one, though, which is unique to data projects and at the root of all failed projects. It’s what I call the ‘rabbit hole question’. If a data project begins with this, it is likely to fail.

The Rabbit Hole Question

This is the question I most often hear from companies wanting to start data projects. It is some variation of – ‘What can I do with my data?’. I agree that it is the most natural question to ask, however, not the question that is going to set you up for success. It is the dream question for the salesperson who can now engage the solution architects. Who will then build an exciting solution. A solution that is likely to cost a lot of money and take a lot of time. Worse, it may not yield any results. Why? because it’s the ‘rabbit hole question’.

This question propels everyone to start thinking about what all to do with the data. Or, where all to apply the algorithm or the tool. There are many possibilities and hence many potential projects. But there is no way to figure out what we will get at the  end of these projects. We will only discover it as we go along. And chances are we may not like what we see in the end, if we see anything at all.

Avoiding the Trap

So, how do we avoid the ‘rabbit hole question’? Where do we start and how do we proceed to maximize our chance of success? The answer is to flip the question – ask “What can my data do for me?”. Better still, use a top down approach of starting with your Business Objectives. The graphic on the right illustrates a more sensible approach to data projects.

info-03-e1583772519476-300x219 Starting a Data Project

The key is to break down the process into two phases – Planning and Execution. Planning requires little time but a lot of thinking but is crucial for success.

It is important during planning to stop thinking about the data you have and what to do it. Instead, start with what the key objectives for your business. Next is to think about the Actions required to achieve those objectives. That leads us to thinking about the kind of decisions we need to take. Then we can ask the question – “What insights do I need to take these decisions?”. These required insights then lead us to the relevant data and findings.

In this process, we may find that we do not have some of the required data. We can then start collecting those. In the meantime, we can then switch to execution with the data we already have. We can use the data and findings to generate relevant insights. These insights then drive the appropriate decisions. These decisions then guide us with the required actions to achieve our objectives.

Data Strategy Workshop

In our experience, the knowledge required for Planning is available in the organization. It usually sits in different silos though. Also, we find that the key stakeholders are usually not aligned.

Hence, we recommend conducting a Data Strategy workshop. Such a workshop aligns all stakeholders around the business objectives. It then allows the group to connect the objectives all the way to the Data they have.

Screen-Shot-2020-03-09-at-9.40.40-AM-300x78 Starting a Data Project

The outcome of the Workshop is an aligned Data & AI Roadmap. We can then jump into execution with the least effort and cost. The initial success then builds confidence in the organization for further projects. It also frees up time of critical resources to contribute to these projects.

Screen-Shot-2020-03-09-at-9.34.57-AM-300x79 Starting a Data Project

Optimus has already conducted Data & AI workshops for various organizations with fantastic results. If you would like your organization to have a clearly defined, cost effective, Data & AI Roadmap, please contact us at rajeev.roy@optimusinfo.com 

 

 

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.

How Cloud is Cooking Up Change in the Restaurant Industry

As more and more restaurant owners look for ways to cut costs, improve customer service and drive loyalty initiatives, application modernization including cloud applications, mobile apps and predictive data modelling are featuring at the top of their personal “must do” menus. Software companies and ISVs that serve the restaurant and hospitality industry are in a race to offer more, by migrating older legacy apps to the cloud and taking advantage of real-time data analysis tools to offer more features and functions to their customers – all while ensuring security and data privacy are still the “special of the day”.

Patrick Albrecht owns and operates four restaurants in Atlanta, Georgia. At one point, he says, he had tens of thousands of receipts, invoices, health department documents and payroll data stuffed into the various offices in his restaurants.

Born in the Cloud is Disrupting the Restaurant Industry

New “born in the cloud” restaurant applications are coming into the market. That is great news for restauranteurs. They will have a choice of vendors to select from – vendors who can offer new capabilities and new functionality that restaurant customers demand.

If you are a team supporting restaurant clients on an existing legacy application, you might need external help if you want to accelerate your own application modernization efforts in order to take on these new competitors.

Legacy restaurant applications, without an achievable plan in place to modernize quickly, are at risk of customer erosion.

By subscribing to [i] a cloud-based mobile app that could organize his overwhelming mountain of paperwork, he figures he’s now saving 2% each month in overhead costs from his four food operations. He can find any document in a matter of seconds and he can compare vendor delivery prices to determine who is giving him the best deal.

Albrecht is one of a growing number of smart restaurant owners who have embraced and employed some of the new cloud-based technologies to make operations more efficient and cost-effective.

Threats Abound

Restaurant chains and food companies that own various restaurant brands know how tough the business is. Competition is fierce, customer loyalty is fickle and profit margins are skinny. The difference between red and black on the bottom line is often as thin as the edge of a knife. Having the right information at the right time from POS, loyalty, vendor management and surveying systems is critical for owners who need to make decisions that can mean the difference between remaining in business and shutting the doors.

Social, Mobile and Data Drive Transformation in the Restaurant Industry

Cloud technology and third-platform innovation accelerators[ii] like social, mobile and big data are changing the landscape for the restaurant industry. Large chain firms and independent operators have experienced how mobile apps can boost business simply by making it convenient for customers to order, pay and review their experience, all with a smartphone.

For many restaurants, they have either directed their internal software developers to modify existing applications or they are looking to their existing POS vendor to do it for them.

There’s just one problem: many of these existing applications were designed long before cloud, social and mobile were part of the landscape. Moving a legacy application into the cloud while taking advantage of new functionality AND ensuring that security and data privacy are paramount is tough to do.

It requires specific skills and knowledge. Few companies can say they are masters of all the aspects needed to modernize.

What to Expect with Application Modernization

Data Available on Any Device

Restaurant owners Matt and Dianne Kraft can now check restaurant sales on their mobile phone while sitting beside a camp fire on a family vacation. “We rarely used to take vacation,” Dianne admitted. “We needed to be at the restaurant to stay on top of everything. But now we can see exactly what is happening in real time; we can even monitor problems as they happen. For example, issues that used to be buried – like a high number of orders being returned – are flagged. We can simply make a quick call to determine if it is a kitchen problem or a server problem, and by working with our manager, get it resolved quickly before customer satisfaction is impacted.

The ability to use cloud to amass multiple points and sources of data is making an enormous difference in the restaurant industry today. Cloud enables data pools to drive data analysis, unavailable before now. Restaurants can profile their customers in fine detail and then design promotions targeted specifically at them. Many legacy apps can’t achieve this sophisticated level of analytics because they weren’t written with current analytic models in mind.

Roughly 60% of consumers are making a restaurant selection – and a reservation – based on what they find on apps like Yelp and OpenTable. Because these apps are using cloud technology, the data these restaurants collect can determine booking patterns, eating tastes, the age of their customers and even their income levels. That’s valuable information for restaurant owners, who can harness this data and use it to drive promotions, communications, messages and offers.

Some cloud-based apps alert a restaurant when a customer is in the area by using what’s called geo-fencing. This creates an opportunity to immediately transmit a mobile ad to that customer.

A POS Cloud Facelift Cuts Costs, Boosts Sales

POS systems are another instance of cloud technology reducing costs.

Application Modernization – Legacy Moves to the Cloud

A large software vendor designing legacy POS applications for more than 30 years turned to Optimus when they needed to modernize their legacy POS. With more than 40,000 global customers to support, their internal IT team not only did not have the time to do the migration themselves, they also did not have the specialized expertise that this migration and design effort demanded. The results? By partnering with Optimus, this client has:

  • Reduced time-to-market and hosting costs
  • Capability to scale on demand to respond to peaks and seasonal fluctuations
  • Rolled out their new cloud app without disrupting existing customers
  • A future-proof roadmap with proximity to PaaS for machine learning, IoT frameworks, Blockchain technology and more.

According to Restaurant Insider[iii], cloud-based POS works for any size of restaurant. Updated sales, inventory, customer feedback and other real-time data can be accessed from the cloud via a mobile device, anywhere. In other words, there is no need for costly on-premises servers, holding valuable and often confidential data.

But moving your legacy application to the cloud is not as simple as a “lift and shift”. You need to assess the code base, making changes to it to take advantage of cloud services while also optimizing the application so that it runs more efficiently in the cloud.

The right upfront efforts mean greater agility and cost savings while taking advantage of trends such as machine learning and blockchain.

Mobile Ready Everywhere

mPOS is a payment system which allows customers to pay by using a mobile wallet –  simply a smartphone that contains encrypted credit or debit card information. Such a system appeals strongly to tech-smart millennials, who control more than 30% of gross income today [iv] and who eat out frequently.

When a food company fails to keep up with the capabilities that modern technology can offer, they are at risk of losing clients and profits very quickly. These restaurants will look for software vendors who can help them.

Software application companies that have legacy applications and cannot quickly make the shift to the cloud will fall behind the technology curve. Their clients will either fall behind as well or abandon them for another vendor.

Plan for Success

Because modernization technologies for the restaurant industry offer so many opportunities that you can take advantage of, it is vital to talk with a company that works in this space every day, delivering custom-tailored solutions and driving cost savings for clients.

Optimus Information delivers cloud-supported application development for the restaurant and hospitality industry. Optimus works on the leading edge and provides your company with full software development and testing, mobile development on any platform, and business intelligence designed to suit your needs and wishes.

Contact us today for more information and let us show you how we can employ our skill with cloud technology to drive your business forward.

 

[i] https://www.entrepreneur.com/article/223562

[ii] https://www.idc.com/promo/thirdplatform/innovationaccelerators

[iii] https://upserve.com/restaurant-insider/benefits-cloud-based-restaurant-pos/

[iv] https://www.michaelhartzell.com/restaurant-blog/how-restaurants-can-use-mobile-payment-technology-to-increase-profits

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/