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Big Data For Small Businesses: How They Give Companies An Edge

While oil was the most valuable commodity available in the 20th century, data has snatched the crown for the 21st century. Businesses receive greater insights regarding their markets by integrating the Internet of Things (IoT), Big Data, and other progressive technologies across all operations to improve productivity and workflows.

Less than two decades ago, this would have seemed impossibly futuristic, but rapid innovation has brought us to a highly critical juncture for small businesses.

Today, small business owners stand at the precipice of great opportunities that were only a faraway dream in the past. With Big Data by their side, leveraging prospects and information becomes an opportunity to create winning strategies. Whether they use in-house data warehouses or have access to business analytics software, there are infinite possibilities for success.

In this blog, we are going to take a look at the reasons why adopting big data modalities will ultimately help small businesses gain a competitive edge over their competitors.

Improved Workflows

In the past, one of the biggest pitfalls for a small business would be the lack of scale and cohesiveness to conduct production activities like their larger counterparts.

Moreover, managing employees across floors and shifts was complex, especially with impending deadlines and delivery timelines.

Today, Big Data has made streamlined workflows simple, with accessible details of every shift worker. From the hours they clock to their availability and work done, monitoring the output can help reduce time consumption, overstaffing, or understaffing.

A Boost in Operational Efficiency

Any business owner with a small production line wishes to expand the production capacity. And it can be done with the help of Big Data and Machine Learning. The technology combined with sensors posted across production and assembly lines can capture data regarding the production process such as approved and rejected outputs, downtime, and wastages.

By taking stock of the data accumulated, a manager can adjust the input and output within shift times to adhere to orders received, thus maximizing the capacity when needed while making provisions for known downtimes, and reducing wastages to manage output quality.

Real-Time Infographics

Every company collects a staggering amount of data each day whether it is regarding their customer leads or production specs. No organization can go a day without gathering such data sets.

However, these rarely get used productively due to the sheer volume and lack of support to convert data into actionable insights. With human labor and generic tools to collect data, assimilate, and create information, logs can be extremely taxing.

However, Big Data platforms and service providers have in-built analytics as their functionality that can help a business use all the gathered data which would otherwise be siloed. By combing through quintillions of terabytes collected from operations and competitors, Big Data analysts can help find all the factors that affect your business and provide insights as comprehensive visualizations to help the management make major decisions more effectively.

Increased Employee Retention

Employee turnover is a huge challenge that several companies are facing for the last couple of years. What is known as the Great Attrition is now becoming a huge stressor for businesses that invest millions into employing the right talent.

The same has also affected small businesses who hire critical roles and train these candidates only to find out it was a bad hire.

What is deemed a bad hire? A candidate who does not fit into the role has a lower output than what was determined or is simply not adept enough despite training. Big Data can help a company comb through and analyze tens of thousands of job applications to connect you with only the most accurate match to the role, thus reducing your attrition rates.

Trend Analysis and Forecasting

One of the biggest letdowns for small businesses is not being able to hop on to trend bandwagons early on. For any business, knowing what’s coming next is perhaps one of the biggest markers of decision-making.

Big data can help small companies catch trends and ride the wave to build momentum in the market. This not only makes a business receive greater visibility but being able to anticipate changes also helps business owners create remarkable thought leadership in the market.

Research Optimization with AI Tech

Research in the past took ages due to endless testing and development practices. However, the process today is highly streamlined due to Artificial Intelligence that helps entrepreneurs conduct multiple calculations simultaneously to arrive at a working variable.

As another example, a small pharma company looking for the right formulation would have struggled in the past due to a lack of extensive infrastructural provision. Today, however, it can get the job done quickly with the use of big data and AI, avoiding lengthy trials.

Wrapping Up

The role of Big Data analytics in any industry vertical can provide substantial scope for business expansion and enhancement. Small businesses can leverage Machine Learning and Artificial Intelligence to find big opportunities without any of the conventional barriers or limitations to trade.

Today, you will find start-ups and solopreneurs diving deep into Big Data in order to double down on every aspect that contributes to their long-term success. This was only exclusive to bigger sharks in the economy’s proverbial ocean.

About Artha Solutions

Artha Solutions is a premier business and technology consulting firm providing insights and expertise in both business strategy and technical implementations. Artha brings forward thinking and innovation to a new level with years of technical and industry expertise and complete transparency. Artha has a proven track record working with SMB (small to medium businesses) to Fortune 500 enterprises turning their business and technology challenges into business value.

The Evolution of Digital Transformation Services in Banking

Shifting from traditional banking services to the digital space is only one aspect of digital transformation services.

The way in which banks and other financial entities engage with, appraise and reward customers has to change significantly. Understanding digital customer behavior, preferences, likes, opinions, explicit & implicit demands, and goals are the first step in implementing an effective digital transformation strategy.

As a result, banks need to shift from a product-centric to a customer-centric perspective.

Evaluating financial consumers in the digital world provides insight into today’s digital consumer interests. Consumers are increasing their standards when financial firms appear to be marching in cahoots. And if their needs do not meet, they are prepared to switch banks.

The Omni-Channel approach is the most efficient digital transformation modelto perceive and transform the entity from traditional banking to digital banking.

Omnichannel includes more than just offering different ways of transactional options. It is a multichannel technique in customer relations that positions the customer at the core of the integration while maintaining a powerful connection between all the channels.

Perspective on the Banking Industry

In the current environment, offering customers a unique and valuable experience has taken precedence over solely offering financial services.

Even without asking the consumer directly, the banking industry must comprehend the underlying demands of the customer in the same manner that airlines or merchants realize the likes, dislikes, and choices of customers.

New technology and devices are providing multiple consumers with contact points every day. Banks must recognize how they use the information paths left behind every time they interact with devices or screens to improve their bottom line. Transaction completion has historically been the priority of most bank operations, including time and money, and is now a significant fundamental aspect of their overall business.

We think banks can benefit from how merchants perceive the customer journey of digital transformation services via an Omni-channel perspective while ensuring that quick, reliable, and accurate transaction processing is still essential. Banks should now reassess how they treat their consumers, perhaps from the perspective of those industries that place a high value on the customer experience.

Importance of Omnichannel

Presently, different channels operate independently, but it’s time to break down these barriers and upgrade the banking experience by implementing an omnichannel approach.

This strategy focuses on a single brand name that brings almost every consumer a customer-centric experience – based on their interests and activities – exactly like an individual bank would for each customer. The execution is so perfect that it becomes inseparably entrenched in the customer’s lifestyle.

Four Main Advancements in Banking Services

To offer customers cutting-edge financial services, digital expedite the creation of a decentralized digital world. Tech firms, retailers, telecom companies, businesses from other industries, and conventional banks can collaboratively benefit from services provided by digital transformation.

The following trends will define how banking services are available in the future.

Engagement Banking

Engagement banking is incorporating services into people’s lives and enterprises.

The expansion of the range of touchpoints that users interact with has been made possible by the internet. Previously, branches and ATMs were the only physical mediums via which banks and customers could communicate.

However, these communication points have substantially expanded thanks to the services of digital transformation and mobile banking. Access to necessary financial services through numerous, well-known non-bank services should be available whenever and wherever they choose.

Platform Banking

Platform Banking delivers plug-and-play banking capabilities.

Front-end services are the area of expertise for newcomers to the financial services industry. To fulfill the diverse needs of their customers and give a superior customer experience, they are concentrating their management resources on offering user-friendly services. This is due to the fact that new entrants give flexibility and affordability top priority when choosing their banking services.

Back-end service providers are creating banking functions on highly scalable cloud architecture to fulfill these needs. They provide these solutions to non-banks via APIs. Convergence in the pursuit of scalability is likely to occur since the flexibility and affordability of these platforms’’ banking services determine how competitive they are.

Online Banking

Online banking provides effective services based on technology.

There’s a huge potential in robotic process automation (RPA) – which enables the automation of routine tasks – and artificial intelligence (AI) technologies – with even more advanced computational power – to significantly change existing labor-intensive systems and processes in the financial sector.

According to a major U.S. financial firm, AI and other technologies will replace many banking professions due to the services of digital transformation. The top US investment bank has also cut the number of human traders working at its headquarters. Instead, Computer programs perform the majority of trading tasks.

Big banks in other countries have expressed ambitions to deploy artificial intelligence and other cutting-edge technologies to reduce staffing levels in the next ten years.

Social Banking

Social Banking is establishing new services without using mediators.

Digital technology eliminates boundaries between people and creates spaces for them to interact directly, even being geographically distant. Additionally, it modifies economic growth by experiencing peer-to-peer exchanges without the use of mediators. Digital transformation services are gaining attention as a new form of economic activity that can directly connect various service providers and users via a platform.

Businesses must collaborate with reliable partners to co-create and take advantage of the business prospects brought on by services of digital transformation and capitalize on the emerging trends in banking services.

About Artha Solutions

Want Enterprise Efficiency? Look Out For Digital Transformation Trends!

Today, the Internet of Things and Cloud technology govern business operations across industry verticals, no matter which sector they belong to. With the rapidly changing demands of customer 2.0, focusing on operational excellence also requires you to establish a healthy workflow. While the old ways are starting to show how they cannot keep up with the evolving state of global business, it is now time to fully invest in an immersive digital transformation strategy that can help your enterprise stay relevant and efficient.

If you’re looking for ways to stand a league apart from your competitors, the best way is to look up the digital transformation trends of 2022 and catch up with what seems to be missing from your strategy. This blog will help you cover all the bases, so let’s get started!

Federating Digital Teams and Platforms by Democratizing Innovations

In the last decade, digital transformation initiatives have been doubling down on Cloud tech, big data, and DevOps. Businesses are largely trying to centralize the three functionalities with modalities like centers of excellence. Companies are now required to incorporate these capabilities for process acceleration and scaling various transformation endeavors, bringing them a step nearer to their value streams as well as buyer product groups.

How can this be achieved? An organization can adopt team topologies that can assist in structuring groups into stream-aligned, platform, or enablement teams. A successful expansion of digital innovation requires these teams’ to set up technological architectures that can mirror the quality of federation such as micro-front ends, data mesh, and cloud landing zones.

Pursuing Organizational Agility

With companies vying to improve their deliveries to multiple parties involved (a.k.a the customer, developer, and employee experience respectively) whilst democratizing innovation, adopting Agile and DevOps principles are imperative.

On the journey to becoming more customer-centric and maintaining a direct line of sight of the consumer base, business leaders and managers can attain higher visibility. This requires the company’s teams to reconsider their team topologies and hierarchical structures to shift towards mature product teams and value streams. This change can help organizations use the best aspects of DevOps, Agile, and product-centric reflection to help bring about a digital transformation beyond technological territories.

Establishing a Digital Fintech Core

Today, the basics of digital transformations revolve beyond the operational aspect of an organization. If you wish to provide digital products and services in accordance with the company’s product and consumer customer experience (CX) strategy, every business, irrespective of the industry vertical requires a digital fintech core to help underpin its operations.

This digital fintech core should be able to:

Manage heaps of customer data effectively
Handle customer authorization and authentication
Provide exceptional omnichannel client operations
Embark on a data, cloud-native, and APIs-first strategy in the context of product building and development.
Providing an excellent developer experience as well as a portal to bring more efficiency to workflows and help teams extend on existing services, products, and data sets.

Companies who can successfully establish this core can experience benefits like:

Providing a seamlessly connected digital customer journey
Deliver multi-channel secured services to clients
Help accelerate the go-to marketplace and roll out compelling products for their consumer base by allowing digital native startups to work with existing incumbents.
Make way for dynamic innovations across the organizational environment by letting partners and clients alike use their APIs to create their own platforms and integrations, thus helping in rapid scaling.

Establishing Cloud as the Digital Principle of Transformation

Organizations are no longer debating the function and role of the cloud ecosystem in their operational strategies. Today, several market leaders have actively expressed their opinions through data-backed thought leadership concerning the pivotal role that the cloud. This also goes as far as to talk about how focusing on the could aspect can underpin the digital foundation that every company will require to extend its capability to offer products, services, and experiences digitally. At the same time, these modalities need to show they are continually scaling without putting the security or reliability factors on the back burner.

The Growth of Multi-Cloud Adoption

Businesses are going to be more open to adopting multiple-cloud providers since it is already a prominent practice in the industry Albeit there is a key aspect that separates a successful multi-cloud adoption from a poorly implemented one: It depends on the way companies manage and use these cloud providers for multiple purposes.

An example of a successful venture would be implementing a multi-cloud control plane that can help democratize the consumption of these providers. Such a platform should be able to facilitate cloud providers to manage and govern their usage while facilitating the internal development and product units to utilize the correct platform for better workflows.

Wrapping Up

Understanding these trends in 2022 will not only help your business make the most of the remaining financial year but also gear up for the year to come. The year 2023 will have digital transformation growth and trends that succeed the present ones, helping leaders anticipate these changes and be future-ready rather than follow suit. By taking up the help of industry experts to establish your digital transformation, you can usher in the era of enterprise efficiency across the board.

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What is Enterprise Data Management, and How Does it Help?

Whether it is a start-up or a well-established business giant, they all need to handle and manage a large amount of data. Mishandling of data can create chaos and disturb the smooth functioning of various departments, leading to poor outcomes. To avoid such mishaps, companies from different sectors rely on Enterprise data management.

According to a report, the market for global enterprise data management is expected to grow at a CAGR of 14.0% from 2022 to 2030. It is because of the increased requirement for data management in enterprises that this market is witnessing fast growth.

Enterprise Data Management

Enterprise data management is a system obtained by an organisation to store, retrieve and access information and data for internal as well as external uses.

Data means any piece of information generated digitally.
The data can be sales volumes, tweets, Instagram videos, posts, pictures, etc. It can be found anywhere around us. Data is the main ingredient in data management, no organisation can run without data.

Enterprise data management or EDM also includes the capacity to transfer data among business partners, branches, or within any department of an organisation. In other words, it simply means how you control your data, who can access it and how you can store it.

Examples of EDM

Keeping and maintaining a record of the IDs who logged into the company’s website to track cyberattacks.
Keeping a record of the transactions, customer orders, and billing to track the history of the customers.

Components Of EDM

Data governance – It is the process that ensures that the data is managed as per the standard norms and policies.
Data integration – Data integration means storing the diversely located data in one accessible place.
Data security – Data security means providing authenticity of the data, the data can be viewed and accessed only by those who are authorised to view it. This component also restricts the external individual to view the secured data.
Master data management – Master data management refers to the ability to ensure that the data must be circulated and shared across the organisation in an updated, accurate, and error-free way.

Many companies across the globe are looking for enterprise data management strategies. There are tons of information available that an organisation has to manage. Companies that opted for enterprise data management have benefitted in multiple ways.

How Enterprise Data Management is helping organisations?

EDM ensures cost reduction through improvement in operational efficiencies. The cost of storage, backup, and restoration can be reduced substantially due to EDM

Decision-making can be enhanced as the quality of data is maintained in EDM and the management can use this data to make future projections.

Easy access to information without any hassle makes it a user-friendly system. Data can be easily accessed anywhere across the globe.

As the information is available at the right time and right place so the management can take future decisions easily.

One of the beneficial features of EDM is the Accurate storage of data that quickly analyses the data and identifies anomalies and mistakes in storing the data. This helps in avoiding data mishandling and keeping the information sorted.

Large data can be stored and retrieved easily.

Faster response to compliance and fraud can be identified as early warning signals that can be shared by EDM to organisations.

The data can be much more secure than it was in manual record keeping. The chances of losing or forgetting data are reduced.

Conclusion

EDM reduces the threat of losing confidential data and ensures security. It has made the data management process much easier removing the extra need of hiring people to manage records offline. But, enterprise data management also needs regular improvements, and updates to remain efficient. This requires a technically sound person to handle and store the data in the digital system.

Although, with its numerous benefits like keeping the data secure, accurate, and high-quality with fewer expenses, EDM is now becoming the first choice for enterprises for data management.

Hence, EDM can be considered as a backbone of the business as it is helping to track the business records from starting to till the end and governs the business on the path to success.

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Customer 360: The Master Data Management Solutions SMES need

The concept of ‘customer 360,’ or having a single view of all your customer data, is gaining traction in trade publications, analyst circles, and even mainstream media. But what exactly is a customer 360?

Even though the notion of “customer 360” is much narrower than Master Data Management Solutions, it has acquired support among IT professionals, business leaders, and the media.

It’s simple to see why. As consumer data accumulates in ever-increasing amounts, it frequently gets fragmented, duplicated, inconsistent, incomplete, ungoverned, and out-of-date as it travels throughout the business.

What is Customer 360?

Customer 360 is a master data management model that aggregates all the data about the customer to form a master customer record.

Customer 360 gives you a single, trustworthy view of a customer’s name, address, phone number, gender, and interactions with a company. Purchase history, billing, service difficulties, social presence, and channel preferences can all be included in the view. This information can be used to guide engagement strategies, customer journey steps, messages, personalised offers, and deliveries.

Organizations can generate value, gain a sustainable competitive edge, and optimize new customer acquisition prospects by using a Customer 360 view, whether in-store or online.

Which channels are involved with Customer 360?

Customers can use the following channels:

In-person, at a company-owned, physical site
Order via phone through a sales affiliate or partner.
Online, via a company website or a mobile application.

It’s critical to know who your customers are and how they interact with your company, regardless of which communication channels they use. McDonald’s, for example, should be aware that the same customer is communicating with them whether through a mobile app, the McDonald’s website, a phone call, or the cloud – even across various retail locations.

This example exemplifies why it’s important to carefully assess which data should be included in a customer 360 view.

Whatever data is involved must give the enterprise this visibility so that customers have the same experience regardless of how they interact with the company. In this situation, McDonald’s must swiftly combine data from numerous transactional sources in order to identify the elements that influence customer relationships and choose which channels they may use to improve customer service.

How Customer 360 benefits your business?

Having a comprehensive, 360-degree view of your customers and interactions with them has economic, “defensive” advantages such as greater profits via better cross-selling and up-selling chances. They also enable a better consumer experience for more tailored marketing, resulting in improved conversion rates and income. A 360-degree customer view also lets you assess customer profitability across different products and services, allowing you to effectively categorize your customers based on relevant data and trends.

In addition to reducing the risk of issuing unfavourable credit terms and regulatory non-compliance, especially with new customer privacy regulations requiring organizations to secure personal data and provide it to customers upon request, creating a customer 360 helps with general risk management, which is critical for financial service firms.

Common Customer Data Problems solved with a 360 Customer View

Multi-channel communication only works when the underlying customer data and how it is managed are consistent. It’s challenging to maintain consistency when data about the same customer is scattered over multiple IT systems, each of which may contain data for every customer communication channel. Regardless of how many sources are included, true customer 360s provide consistency.

Customer data quality is another common challenge that customer 360 views can assist alleviate. The same customer’s name (or address, phone number, or other information) is frequently recorded differently in multiple systems or even inside the same system.

Customer 360s are designed to solve this problem by allowing users to determine which person or business comprises a customer entity and include their attributes in order to have more informed interactions with them. Because there is no consistency in how customer records are handled, leveraging third-party external sources like Dun & Bradstreet or social media data becomes difficult without it.

Why do I need a Customer 360 view?

A Customer 360 view is something that can be termed the cost of maintaining, satisfying, and increasing your customer base. It provides a set of universal benefits while also assisting in the resolution of challenges unique to businesses like manufacturing, finance, and retail. To optimize the customer experience, integrate Master Data Management Solutions with structured and unstructured data to achieve a 360-degree customer view regardless of your industry or vertical.

You may discover which of your customers are failing and attempt to enhance your gross margins by evaluating your profit margins by industry, account, and even individual customers. Understanding this allows you to better segment clients in order to reduce risk and increase profits.

A customer 360 view allows you to improve retention rates by enhancing your grasp of who your customers are. This information is crucial for raising marketing conversation rates by contacting the right customer with the right message at the right time and increasing revenue generation through relevant cross-selling and up-selling opportunities.

Increased adaptability to business and economic environments is another common benefit. Customer 360 views enable firms to respond to events like the COVID-19 pandemic by decreasing expenses while using the most meaningful customer connection channels by immediately providing them with all essential information about their customers.

In conclusion, effective Master Data Management solutions allow businesses to maintain a 360-degree view of customers and their journeys with the product. Leveraging a customer’s data to anticipate their needs and develop strategic communication plans is essential for long-term development.

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How to Choose the Right Managed Cloud Services Provider for Your Business?

Businesses are increasingly relying on cloud services to support their business infrastructure (databases, performance, storage, networking), software, or services to support performance, flexibility, innovation, scalability, and provide cost savings at the same time. In order to support system reliability and remote work and also meet the demand in the market for personalized customer experience, around 90% of organizations have noted a massive surge in cloud usage amid the accelerated digital transformation due to Covid-19.

The whole process of selecting a managed cloud services provider for your organization can be very complicated because of the absence of a common framework for assessing those CSPs with the fact that no two CSPs are the same. To help you get through with it, we’ll be discussing several factors that are needed to be taken into consideration to identify a CSP that can best match your technical, business, and operational needs.

Factors to Take into Consideration while choosing a CSP

1. Cloud Security: You should know exactly what your security goals are, what security measures each provider offers, and what strategies they utilize to secure your apps and data. Also, you must ensure that you understand the exact areas in which each party is responsible. Consider what security measures are included free of charge with each vendor you’re considering, what additional premium services are available from the providers, and where you might need to augment with third-party technologies. Since security is a key priority in the cloud (and everywhere else these days), it’s vital to ask specific and extensive questions about your specific applications, industry, legal needs, and any other issues you may have.

2. Cloud Compliance: Next, make sure you select a cloud architecture platform that can assist you in meeting industry and organizational compliance criteria. Whether you will comply with GDPR, SOC 2, PCI DSS, HIPAA, or any other standard, be sure you know what it will take to attain compliance once your applications and data are hosted on a public cloud. Make sure you know what you’re responsible for and what areas of compliance the supplier will assist you with.

3. Architecture: Consider how the architecture will be integrated into your workflows today and in the future when selecting a cloud provider. You should also think about cloud storage designs while making your decision. The three major providers have identical designs and offer many types of storage to meet different needs when it comes to storage, but they all have different forms of archive storage. Each service provides options for regularly storing and accessing data vs. infrequently retrieving data (hot vs. cool storage). Cool storage is typically less expensive, but it comes with many limitations.

4. Manageability: As an organization, you should spend some time understanding what various cloud platforms will require of you in terms of management. Each service integrates with a variety of other services and supports multiple orchestration tools. If your company relies on certain services, make sure the cloud provider you choose has an easy way to integrate them (or that your organization is comfortable porting over to a similar service that is supported). Before you make a final decision, you should figure out how much time and effort it will take for your team to handle various components of the cloud infrastructure.

5. Support: Another aspect that must be properly considered, is supported. Will you be able to receive help quickly and easily if you need it? In some circumstances, chat service or call service will be your only source of assistance. You may or may not find this acceptable. In other circumstances, you may have access to a specific resource, but time and access would most likely be limited. Before you choose a cloud service, ask questions about the level and type of support you will receive as an organization in times of crisis.

6. Costs: While cost should never be the sole or most essential consideration, there’s no denying that it will influence your choice of the cloud service provider(s). It’s worthwhile to consider both the sticker price and the accompanying charges (including personnel you may need to hire to manage your instances).

7. Service Levels: When businesses have stringent requirements for availability, reaction time, capacity, and support (which, let’s face it, practically all do these days), this consideration is important. When picking a provider, Cloud Service Level Agreements (Cloud SLAs) are an important factor to consider. It’s critical for a cloud service user and a cloud service provider to create a clear contractual relationship (read: legally enforceable). Legal requirements for the security of data hosted in the cloud service should also be considered, especially in light of GDPR rules. You must have faith in your cloud provider to do the right thing, as well as a legal agreement that will protect you if something goes wrong.

While the seven factors listed above will assist you in developing a sound analytical framework to use when deciding which managed cloud services provider to entrust your data and apps to, you can add granularity by doing a thorough examination of your organization’s requirements to uncover extra aspects that will help in your decision-making.

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3 Ways To Reduce Data Governance Failures

No organization is immune to data governance failures. They can be costly and embarrassing for businesses when things go wrong. Data governance is a complex process, and there are many moving data governance solutions for enterprises. For example, data governance solutions include data quality, data security, and data compliance. Even if a single part of the data governance process fails, it can have a ripple effect on the entire organization.

Fortunately, there are ways to reduce the risk of data governance failures. There are many measures that can help reduce the risk of data governance failures, such as:

Data Discovery and Profiling
Data Cleansing and Standardization
Data Security and Compliance

Here are three ways to reduce Data Governance Services failures:

1. Deliver trusted data to the people who need it

Data governance is about more than just managing data. It’s also about making sure the right people have access to the right data. For example, Data Discovery and Profiling can help you identify which data is most important to your organization. Once you’ve identified the key data, Data Security and Compliance can help you control who has access to it.

Delivering trusted data to the people who need it, can help reduce the risk of data breaches and other security threats.

Poor and uncontrolled data access is one of the main causes of data breaches. For example, in 2017, the Equifax data breach occurred when hackers gained access to the personal information of 145 million people. One of the main reasons the hack was successful was that Equifax had poor data security controls.

Data Discovery and Profiling can help you avoid a similar fate by delivering trusted data to the people who need it.

2. Ensure data quality across your organization

Data quality is another important part of data governance. Inaccurate or incomplete data can lead to problems down the line. Suppose you’re a retailer and you have a customer’s address in your database. If the address is inaccurate, the customer may not receive their purchase. Inaccurate data can also lead to compliance issues. For example, if you’re required to report data to a government agency and the data is incorrect, you could face fines or other penalties.

There are several measures that can help organizations ensure that the quality of their data is accurate and complete:

Data cleansing: This feature can help you clean up inaccuracies in your data.
Data standardization: This feature can help you ensure that all of your data is consistent.

As a result, these measures can help improve the accuracy of your data and avoid compliance issues. Please note that data quality is an ongoing process. You should continuously monitor your data for inaccuracies and take steps to correct them.

3. Automate data governance processes

Data governance is a complex process, and there are many moving parts. As a result, it can be challenging to keep track of everything. This is where automation comes in. Automating data governance processes can help you:

Save time: Automating data-intensive tasks can free up your time so you can focus on other things. For example, if you’re manually cleansing data, it can take a lot of time. But if you use automating data governance processes, you can automate the process and save yourself some time.

Improve efficiency: Automating repetitive tasks can help improve your organization’s overall efficiency. If you’re manually standardizing data, it can be easy to make mistakes.

Reduce errors: Finally, automating data governance processes can help reduce the risk of human error. Errors can be costly and time-consuming to fix. By automating data governance processes, you can help reduce the risk of errors.

Conclusion

Data governance is a complex process, but it’s important for any organization that wants to avoid data breaches and other security threats. The three data governance solutions mentioned can help reduce the risks of data governance failures in several ways. If you’re looking for a way to improve your organization’s data governance, these measures are a good place to start.

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Future of Data Governance Services: Top Trends For 2022 and Beyond

There was a time in the early 2000s when data governance was not really a thing. Surely, there were pioneers back then who laid down the groundwork for data governance, but it wasn’t still taken seriously. Cut to the present time and Data Governance Services are in high demand.

As the rules and trends of data governance keep evolving every year, let’s look at the following trends that are going to stand out in 2022 and beyond:

1. Operational Data Modelling

One of the most meaningful operational actions to be derived from data governance this year comes from data modelling. Interchanging data between different systems as part of one collective data fabric remains more indispensable now than ever as more companies keep adopting this approach for data management.

Expressive data models that have clear taxonomies and semantics can use machine intelligence to figure out how different schemas of various data systems are blended for frictionless integration. So, you get similar details in various systems and the governance has the maps regarding how it is expressed in these systems. Governance solutions can be involved in real-time in this case.

This particular approach spares time and cost by bypassing the need to write special programs to make the most of what happens in the data governance arena.

2. Metadata Insights

In 2022 and beyond, inferences regarding metadata in the data models will streamline the taxonomies for entertainment and media content engines, for instance, across local and global sources to gain real-time results. There will be quicker automation of metadata inputs thanks to cognitive computing methods. Otherwise, all metadata descriptions are going to be manual.

So, in other words, detailed visibility of metadata might presage events or offer a complete roadmap of previous events to make sure data quality and lineage remains intact. Thus, one can expect the following positive changes related to metadata this year:

Traceability of metadata: The traceability of metadata is crucial for trusting and understanding the details presented in analytics.

Root Cause Analysis: All aberrations and outliers in procedures related to analytics can be easily illustrated through metadata analysis. When someone notes an error or something appears as an anomaly on the dashboard, there will be a graph to show what went amiss.

Impact Analysis: Metadata will be scrutinized for each phase of SQL to extract information through rows, columns, and tables of data. The graph that will accompany the process will outline the exact changes.

3. Activation of Data Stewardship

The fact that data stewards are empowered is a direct repercussion of changing data governance from passive employment to an active one. Modern innovations regarding controlled data access (focusing on data stewards) are essential for speeding up the time necessary to utilize data. At the same time, it is necessary for conforming to the governance standards regarding which users can see which data.

Such shared data governance approaches issue the automated approval and centralized governance regulations in infrastructural setups. For instance, the owner of sales data can decide what part of the data he wants to allow John Doe to access.

Automating this distribution of the centralized governance regulations into decentralized sources can remove the IT bottleneck for data access. It will facilitate low-latent data sharing. Thus, data stewards from Talend Data Management Services – the people who understand the data best – remain at the forefront of delegating and deciding data access.

4. Data Quality

Data quality, along with the attendant features of data reliability and data validation, happens to be the substratum on which all forms of data governance, specifically in an operational setting, depends.

You will not be able to augment or automate processes when your data is not high quality or healthy to start with. Thus, the trend would be to embed a data governance staple like superior metadata management into the operational systems to generate metadata specifics in real-time. It will need proper data validation measures to make sure that it is sensible and adherent to the best practices.

The organization will use different means of ensuring data security and quality at a level that is reliable for operations and traditional decision-making. As the nuances of data governance are changed, the organizations will derive greater profits from their IT initiatives.

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7 Best Practices That Help To Avoid Common Data Management Mistakes

Considering big data applications are growing at such a rapid rate, more and more firms are opting for digital transformation to stay relevant and up to date with the latest trends.

Organizations have acknowledged the value of big data and are treating it as an asset (perhaps the most valuable of all as it has the power to determine growth trajectory and provide an upper hand over competitors), but they are yet to get any valuable insights from it.

True, data leveraging isn’t for everyone, and translating it into information that is consistent, correct, and thorough, is frequently found to be lacking. Many companies throughout the world have increased their enterprise data management efforts in recent years, hiring experts in data management services, but the rate of success of these projects has been discouraging.

Those of you who work with big data will agree that businesses are having a difficult time controlling and making sense of the huge amounts of data that must be controlled and made sense of in order to sustain competitiveness, meet customer needs, and, obviously, comply with the law. The battle to design programs that permit organizational sustainability and determine corporate integrity is real. With effective data management services, this can be easily achieved.

In this article, we have mentioned seven best practices for your business to consider for effective and efficient data management.

Build strong file naming and categorizing conventions

If you want to use data, you must first be able to locate it. If you can’t manage it, you can’t measure it. Create a user- and future-friendly reporting or file system, with descriptive, standardized file names that are easy to identify and file formats that enable users to search for and discover data sets while keeping long-term access in mind.

A typical format for listing dates is YYYY-MM-DD or YYYYMMDD.

It’s ideal to use a Unix timestamp or a defined 24-hour notation, such as HH:MM:SS when listing times. Users can keep note of where the information they need comes from and find it by time zone, whether your organization is national or global.

Consider Metadata for data sets carefully

Metadata is essentially descriptive information about the data you’re working with. It should
include details about the data’s content, structure, and permissions so that it can be found and used in the future. You can’t rely on being able to use your data years down the road if you don’t have this precise information that is searchable and discoverable.

Items in the catalog include data author, what data is contained in this set, descriptions of fields, when and where was the data created, why was it created, and how was it created.

This information will then assist you in creating and analyzing a data lineage as the data flows from its source to its destination. It also comes in handy when mapping relevant data and recording data relationships. The first step in developing a solid data governance process is to collect metadata that informs a secure data lineage.

Data Storage

Storage strategies are a necessary part of your workflow if you ever want to be able to access the data you’re creating. For all data backups and preservation methods, develop a strategy that works for your company. Consider your requirements carefully because a solution that works for a large corporation may not be suitable for the demands of a small initiative.

Consider the following storage options:

Desktops/laptops
Networked drives
External hard drives
Optical storage
Cloud storage
Flash drives

The 3-2-1 methodology

The 3-2-1 approach is a basic and widely used storage strategy. The following strategic
recommendations are suggested by this methodology:

3: Back up your data three times
2: Using two different storage techniques
1: Storing one of them offshore

Without being unduly redundant or complicated, this strategy provides smart access and ensures that a copy is always available in case one type of place is lost or destroyed.

Documentation

We can’t disregard documentation when it comes to data management best practices. It’s generally, a good idea to create numerous levels of documentation that explain why the data exists and how it can be used.

Documentation levels:
Project-level
File-level
Software used
Context

Commitment to Data Culture

Ensuring that your department or company’s leadership prioritizes data experimentation and analytics is part of a commitment to data culture. This is important when leadership and strategy are required, and if budget and time are needed to ensure that adequate training is conducted and received. Furthermore, having executive sponsorship and lateral buy-in will enable better data collaboration throughout your organization’s teams.

Data Quality Trust in Security and Privacy

Building a data-quality culture necessitates a commitment to creating a secure environment with high privacy standards. When you’re trying to provide secure data for internal communications and planning, or when you’re trying to develop a trusting relationship with a customer by ensuring that their data and information are kept private, security is important.

Your management procedures must demonstrate that you have secure networks and that your staff is aware of the importance of data privacy. Data security has been acknowledged as one of the most important decision-making elements in today’s digital market when firms and consumers make purchasing decisions. One breach of data privacy is too many. So, plan
accordingly!

Invest in Quality Data Management Software

It is recommended, if not needed, that you invest in quality data-management software when evaluating these best practices together. Organizing all of the data you’re collecting into a usable business tool can make it easier to find the information you need.

Then you can construct the appropriate data sets and data-extract scheduling to meet your business requirements. Data management software will help you design your best governance plan by working with both internal and external data assets. Tableau has a Data Management Add-On that can assist you in implementing these best practices in your analytics environment.

Using trustworthy software to help you build, catalog, and control your data can help you gain confidence in the quality of your data and lead to self-service analytics adoption. Take your data management to the next level with these tools and best practices, and build your analytics culture around managed, trustworthy, and safe data.

Trying to solve all of your data challenges in the early stages of data management is a recipe for disaster. To minimize difficulties and accomplish your organizational demands on schedule, we recommend going slow and taking baby steps.

Data Governance Vs Data Management The Difference Explained

People often wonder if there is any difference between Data Governance and Data Management. Well, the answer is yes. However, they are related.

Data governance is the definition of organizational structures, data owners, policies, regulations, processes, business terminology, and measurements for the end-to-end lifespan of data (collection, use, storage, protection, deletion, and archiving).

The technical implementation of data governance is data management.

Data Governance Solutions are little more than documentation if they aren’t put into action. Enterprise data management allows processes and policies to be executed and enforced.

Simply put, data governance solutions help develop policies and procedures surrounding data, whereas data management solutions implement those policies and procedures to assemble and use the data for decision-making. To better grasp how these notions work together in practice, it’s helpful to first understand what each of them is.

What is Data Governance?

Let’s look at some aspects of data governance, shall we?

People

People are essential to data governance because they are the ones who generate and manage the data, as well as the ones who gain from well-governed data. Subject matter experts in the business, for example, can identify the organization’s standardized business terms as well as the levels and types of quality standards required for various business processes.

Data stewards are in charge of resolving concerns with data quality. IT professionals take care of the architecture and management of databases, applications, and business processes. Data privacy and security are the responsibility of legal and security personnel. And cross-functional leaders, who make up the governance board or council are in charge of settling conflicts among various functions inside an organization.

Rules and Policies

If policies specify what should be done, rules specify how it should be done. Policies and regulations are used across processes and procedures by organizations; popular categories include consent, quality, retention, and security. You might, for example, have a policy that stipulates that consent for processing must be sought before personal data can be used. When personal data is acquired, one rule might outline the consent alternatives that users must choose (such as billing, marketing, and third-party sharing). Another rule can state that prior to providing a promotional offer to a customer, marketing consent must be confirmed.

Metrics

What is measured will be managed. The number of duplicate records in an application, the correctness and completeness of data, and how many personal data pieces are encrypted or masked are all common technical metrics. While these metrics aid in data technical management, leading businesses are also attempting to define how these technical metrics affect business outcome measurements.

For instance, Days Sales Outstanding (DSO) is a typical business indicator used by financial analysts and lenders to assess a company’s financial health. When client address data is inadequate or faulty, the billing cycle time and, as a result, the DSO will increase. Analysts and lenders may view a higher DSO than the industry average as an indication of risk, downgrading the company’s outlook or raising the cost of financing.

What is Data Management?

Let us now dig into some tools and techniques for data management.

Cleansing and Standardization

Data quality policies can be implemented and enforced with the help of cleansing and standardization. Profiling allows you to compare the data’s validity, correctness, and completeness to the data quality parameters you set. You may then rectify issues like invalid values, misspelled words, and missing values. To enforce data quality at the point of entry, you can also incorporate cleansing rules into data entry processes.

Profiling also aids in the identification of similarities, differences, and links between data sources so that duplicate records may be removed and consistency can be enforced across all sources.

External data, such as DUNS numbers, demographics, and geographic data, can be used to enrich internal data. Many firms also establish a centralized hub to assist in maintaining master data semantic consistency across data sources.

Masking and Encryption

Masking and encryption assist you in enforcing and implementing privacy and protection policies. Tools and techniques for data discovery and classification assist you in identifying sensitive and personal data and categorizing it as requiring protection based on internal requirements and external regulations such as the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and the General Data Protection Law of Brazil (LGPD). These tags can then be utilized to implement suitable security measures. Some users may be authorized to access raw data, while others may require the data to be dynamically masked upon query, depending on classification and access regulations.

Internally and externally, data flow modeling can help you understand how data is acquired, processed, stored, and distributed. Based on classification and privacy policies, you can then decide on relevant protection mechanisms. Data masking, for example, maybe sufficient for access within your firewall, but data must be encrypted before being shared with other parties outside your organization.

Archiving and Deletion

The use of archiving and deletion aids in the implementation and enforcement of retention policies. When data is no longer required for day-to-day operations but is still required to meet regulatory requirements such as tax reporting or long-term storage, it is archived. Data archiving tools also keep track of how long data should be kept, index it for quicker retrieval for uses such as legal discovery, and enacts necessary access and data masking/encryption controls. Data is permanently destroyed after the predefined retention period has expired.

While this may appear simple, balancing the retention needs of industry rules (such as BCBS 239 and CCAR) with the erasing requirements of governmental and regional regulations is a difficult process (like GDPR and CCPA).

While data governance and data management are two separate entities, their goals are the same: to build a solid, trustworthy data foundation that allows your company’s finest employees to do their best job.