As seen on Forbes.

In the digital economy, data is our most valuable resource. “Data is the new oil” is now a popular saying, and it’s an apt metaphor. Companies that learn to extract and use data efficiently have the potential for huge rewards, driving profits, innovation and customer satisfaction.

Just like with oil, quality matters in data. Quality isn’t free; it requires time, money and attention, but it’s worth the investment.

What Is Good Data? 

Good data quality means the data is fit for use based on the business context and requirements. The business rules governing quality include both quantitative (e.g., “Is the date legitimate?”) and qualitative rules (e.g., “Is the date captured in the American notation?”). In addition, expectations of users regarding availability, reliability, usability, relevance and clarity may lead to perceptions of quality issues. Data quality initiatives need to address various aspects to ensure that data is trustworthy.

Think about global data related to Covid-19 vaccinations. Reliable data must include a patient’s date of birth, the date of each dose, type of vaccine, number of required doses and location of each dose. Quality issues become complicated when you consider that some users received shots from different vaccine manufacturers, or data may have been captured in different states or countries. Poor data quality can prevent various stakeholders — including public health experts, vaccine advocates, and the general public — from making informed decisions. If people perceive vaccine data to be unreliable, they may become more hesitant to get vaccinated and ultimately damage public health outcomes.

The Cost Of Bad Data 

In 2016, an IBM study estimated that bad data costs the U.S. economy $3.1 trillion a year, and in 2020, a Gartner survey found that organizations calculated that the average cost of poor data quality was $12.8 million a year.

In my experience leading a global data and analytics (D&A) solutions provider, I’ve seen that while everyone cares about data quality in theory, when it comes to actually making funding decisions, many customers want to cut corners.

But this is where the rubber meets the road. If you don’t finance data quality initiatives, then you won’t get the result you want. Poor quality data can lead to flawed decision making, top- and bottom-line consequences and decreased employee and customer satisfaction. Incomplete data may result in ineffective marketing campaigns, and a data breach can cause reputational damage or leave you vulnerable to litigation under laws like GDPR or CCPA.

Six Common Challenges In Data Quality Improvements

Improving data quality in your company will have significant long-term benefits, but you must be proactive. Here are six of the most common challenges to be aware of when improving data quality:

1. Lack of awareness: Because data is an intangible asset, it’s often hard to assess quality and address problems. Your stakeholders may not fully appreciate the state of data quality in your systems until a major issue impacts your business.

2. Difficulty justifying investments: To get buy-in on improving data quality, you need to be able to make a solid business case for it, showing how poor-quality data has had negative consequences in the past. But frontline staff may not be willing to document quality issues to build a future business case for something like automation, preferring instead to resolve issues manually.

3. Confusing shared responsibility with IT responsibility: Enterprise data is often used across multiple business units, moving through line-of-business systems into reporting and analysis systems. Quality ownership is delegated to IT as data flows through various pipelines, yet IT is not fully responsible for the source systems. Data quality demands shared responsibility.

4. Resistance to change: Data quality programs are heavily focused on continuous improvement, which calls for users in your organization to adopt new behaviors and perform additional checks and balances with discipline. If employees are unwilling to adapt, you will run into obstacles.

5. Fear of transparency: Data quality assessments and real-time quality dashboards may make some leaders uncomfortable. Looking into past data decisions and results may cause some in your organization to feel embarrassed or concerned, creating yet another roadblock.

6. Lack of sponsorship: Data quality initiatives often compete with new technology and product management investments. It can be tempting to throw money at a shiny new object, spending $X on a cloud computing platform instead of the same amount for a data governance consultant’s annual fee. Data quality is less glamorous and often loses out to modernization initiatives.

Five Ways To Improve Data Quality 

Once you’ve identified the challenges you’re facing, here are five actions you can take to address them:

1. Sponsor beyond budgets: To achieve successful data quality initiatives, you must be willing to invest more than dollars. Articulate the importance of data quality for the organization, inspire cross-functional collaboration, prioritize progress and hold frontline managers accountable for long-term success.

2. Invest in data quality and transparency tools: As the volume, variety and velocity of data increases, you need more specialized tools for quality management. Invest in software that automates quality management for profiling, catalogs, metadata and lineage.

3. Adopt DataOps automation practices: DataOps practices build on DevOps practices from the agile software engineering domain within data analytics. Integrate them to promote collaboration, communication and automation to bring business, applications, operations and data teams together to speed feedback cycles for quality issues and reduce cycle times for delivering D&A products.

4. Embrace a stewardship culture: To create a data-driven culture, you need to incorporate stewardship in your organizational norms and habits. This is a shared responsibility among various stakeholders and includes identifying and articulating quality issues and following governance practices to resolve issues.

5. Build a data management team: Data quality management requires dedicated skills, roles and positions depending on the scope, size and complexity of your organization’s data. Hire the people who can evaluate, design and execute the solutions you need, including Chief Data Officers, data analysts, business and technical stewards, tool experts and data management consultants.

Investing in data quality is a long but worthwhile journey. Evaluate your company’s needs and challenges, and invest in the practices, tools and leadership required to advance valuable data quality initiatives.

With the increase in data and a rapidly changing technology landscape, business leaders today face challenges controlling costs, fulfilling skill gaps for employees, supporting systems and users, evaluating future strategies, and focusing on modernization projects.

Here we discuss six reasons why organizations are embracing managed analytic solutions that rely on experts to build, operate, and manage their data and analytics services. These are based on the recurring themes which we have observed and experienced while working with our customers.

  1. Keep costs low: Total cost of ownership for running and maintaining D&A systems has several cost elements like staff costs, operational costs, software + infrastructure costs, and (intangible) opportunity costs like technical debt and avoidable heavy lifting. While cutting costs in the short term may lead to some immediate gains, cost effectiveness in the long term and on a sustainable basis is the end goal. The right way to approach and achieve guaranteed, predictable cost savings is through a potent combination of automation, talent, and process improvements.
  2. Improve system stability and reliability: Missing SLAs, performance issues, frequent and persistent downtimes, and an inability to comply with regulatory requirements are the usual suspects when it comes to areas giving sleepless nights to leaders navigating enterprise data and analytics (D&A) systems. Improving system stability and reliability requires long term planning and investments in areas like modernization of D&A systems, data quality initiatives under a larger data governance program, RCA with feedback, 360-degree monitoring and pro-active alerting.
  3. Intelligent D&A operations: You may want to drive operational efficiency by reducing the piling automation debt, bringing in data-driven intelligence (and human ingenuity) to achieve AI-driven autonomous and real-time decision making, better customer experience and as a result superior business outcomes. An example would be on demand elasticity (auto scaling) to scale-up the processing power of your D&A systems, based on forecasted demand due to seasonality in the business based on past trends.
  4. Focus on core business objectives: You may need to focus on your core business objectives and not get stuck in the daily hassles of incident management and fire-fighting production issues. We have seen that reducing avoidable intervention from your side becomes difficult, especially when you are managing it in-house or using a managed services vendor operating with rigid SLAs. A recommended approach would be to engage with a trusted advisor to figure out the right operating model for managed services with shared accountability and define service level outcomes. This will enable you to devote attention to more innovation focused and value-added activities which drive business results.
  5. Get the expertise you need: Given multiple moving parts involved in successfully running D&A systems, and the sheer flux of technological changes, your business needs the ability to tap into a talent pool easily, and on-demand. If executed well, this does wonders to your capabilities in managing D&A systems and achieving desired business outcomes.
  6. Improve user experience: This is the most important and yet often the most neglected aspect in a lot of cases. In the context of managed services, an elevated user experience entails data literacy, ability to leverage tools to the fullest, clarity on SLAs and processes, trust in data quality, ability to derive value from analytic systems and hence adoption.

Infocepts Managed Services solution helps organizations achieve one or more of these motivations. We help drive digital transformation, handle legacy systems, reduce costs, enhance innovation through operational excellence, and support scaling of business platforms and applications to meet growing business needs. You can rely on our D&A experience and expertise to build, operate, and run analytic systems which help to drive outcomes quickly, flexibly, and with reduced risk.

Get in touch to know more!

Recent Blogs

Using HoloLens 2 technology allows for real-time data analytics in retail stores

Infocepts collaborated with Neumont College of Computer Science to implement Power BI reporting and functionality with Microsoft’s HoloLens 2, allowing users to not only see data on products but also see the product in the HoloLens world.

We interviewed the team behind the project to seek answers on the complex matter of using real-time data to make informed decisions in retail stores.

Q: What motivations do retail stores have to embrace mixed reality for analytics?

Analyzing data on products is time-consuming, unengaging, and inefficient. Retail store managers sit down at a desk and plan out the store without actually being able to see the store or products. They can see the analytics but the actual store layout, feel, and atmosphere is lost when they are distanced from it, making planning from an office that much more inefficient. Workers have to place on the products without much help other than paper layouts, making product placement difficult and time-consuming.

Q: How can a HoloLens 2 app help?

Infocepts developed an application to streamline the process behind range planning, stocking, and to improve the overall understanding of the products on the retail floor. With the app, users can scan a QR code associated with any product to quickly gain access to the data and analytics filtered to that specific product. This allows the user to be more informed when it comes to decision-making. Additionally, users can view a 3D model of each product within the HoloLens environment, allowing the user to visualize the layout of the aisles in real-time.

HoloLens gives retailers easy access to data reports and models of their products while on the store floor, enhancing the relationship between the store managers, employees, and the products. Employees can view models to help find products they aren’t familiar with and real-time data reports help managers make decisions on product placement and orientation all while being able to see the physical layout with holograms projected from the HoloLens 2.

Want to learn more about Infocepts?

Infocepts is a data solutions firm that uses services to drive business outcomes with data and analytics. Through a solution-oriented approach, we guide the modernization efforts of our customers, enabling you to make truly ‘data-driven’ decisions so your users can make smarter decisions and businesses can achieve better outcomes.

To find out how Infocepts can help you during your business intelligence migration and beyond, connect with one of our experts directly.

Find out more about Microsoft HoloLens 2:

HoloLens 2 offers the most comfortable and immersive mixed reality experience available enhanced by the reliability, security, and scalability of Azure.

Recent Blogs

Are you ready to migrate to an Enterprise Data Platform?

Enterprises need to migrate to new analytic systems in their quest to derive maximum value out of their data and hence stay modern. Doing so is not easy due to multiple reasons. What you need is a calibrated approach, coupled with experience and expertise.

To help you make the transition to a modern data platform, Infocepts joined forces with
Cloudera and AWS to deliver this webinar – to demonstrate how leveraging the right
combination of product, platform, and expertise is the key to a successful Enterprise Data

  • What is Cloudera Data Platform (CDP)?
  • Why should you migrate from on-premise or legacy Cloudera platforms (like CDH) to CDP?
  • How AWS works with CDP?
  • AWS services which can be leveraged within CDP in a “plug-and-play” mode
  • Why a trusted partner needs to play a pivotal role in the implementation
  • Overall migration framework, key challenges and considerations


Organizations are becoming more data-driven to take business decisions faster than ever before. Business users expect quick interpretations of their data to act on insights in real-time. This is where data storytelling kicks in. Data alone, can’t tell you insights, but through data storytelling techniques you can better understand the data and get faster and easier access to key business insights. Data storytelling is about creating a compelling narrative anchored by three basic elements: Data, Visuals, and Narratives. Data-driven storytelling often involves the use of visualization techniques to support or complement a written or spoken narrative. In this blog, we will dig into why narratives in data storytelling are so important.

What are Narratives in Data Stories?

Effective storytelling with data moves the audience to understand the core of your insights, understand the key takeaways, and at the end act on your recommendation. Story narratives are nothing but written messages or annotation of the key data insights. Story narratives play an important role in convincing your audience to understand the insights and persuade them to act on them. Mostly narrative acts as a supporting element to visual stories, which goes well with other data visualization. Narratives can be static, dynamic, or automated depending on what type of data story you are telling your audience.

Static Narratives:

Static narratives are primarily used to convey key insights along with visualizations using simple, everyday human language. A static narrative approach is used to tell explanatory stories to your audience. You can get your point of view across clearly to your audience using simple narratives along with effective visualizations. The static narrative follows a linear approach in which data is presented in chronological order using a traditional story arc with beginning, middle, and end. It focuses on heavy messaging and clarity of thoughts and a specific call to action from the audience’s perspective. Typically, you will find such a narrative approach followed in author-driven data stories such as data journalism, PowerPoint decks, infographics, and static representations of data. Though this is the most common and standard approach of telling narrative stories at times it might give biased perspectives because of the inability to deep dive into data.

Let’s see a static narrative example:

Dynamic Narratives:

Dynamic narratives can be a good alternative option in absence of automated narratives to create basic narratives to support your visual story. These focus more on the explanatory analysis by highlighting key KPIs and other important data insights using pre-defined dimensions and measures. This works as an out-of-the-box feature of an analytical tool to summarizes visualizations or report narratives that are customizable. These narratives are not auto generated so they often require more manual effort to craft sentences using static and dynamic text fields. You can map the text to existing fields, dimensions, and measures and get the values driven through charts or filters. You also have all basic formatting options available to highlight the key insights which require more attention from the end-user perspective. This option is good for telling short dynamic narrative stories in absence of automated Natural Language Generation (NLG) driven narratives.

Here is a dynamic narratives example:

Automated Narratives:

Next-gen analytical tools highlight automated narrative features in their analytical apps to quickly summarize visuals and reports by providing built-in customizable insights. These features are highly dynamic and with one click they automatically generate a summary of your key insights from your visualization. It combines the art of data storytelling with Natural Language Generation (NLG) capabilities. NLG capabilities interpret the data and generate English commentary (narrative) on the underlying data as a result, instead of trying to derive patterns or relationships from standalone visualizations, users can act on NLG-powered recommendations. Such a machine learning-powered narrative approach allows the end-user to interact with data, ask questions, explore, and tell their own data story. It is good for explanatory and exploratory data analysis.

Here is an example of automated narratives:

Overall narrative plays an important role in laying a solid foundation for making data-driven decisions by giving a glimpse of the data story in a more structured manner to display the hidden truth behind the data.

What to learn more? Get Started with our Data Storytelling solution to accelerate your data-driven decisions.

Recent Blogs

A cloud migration is the process of moving applications, data, and other components hosted internally on servers to a cloud-based infrastructure. Whether you are a “Cloud proponent” or a (non-vocal) opponent, chances are high that your organization has already embarked upon its cloud journey as it is becoming a top priority for most businesses.

Different Types of Data Migrations

Traditionally, a cloud migration strategy is considered as moving a virtual machine from on-premises to Cloud. However, recently, there are rise in different use cases such as cloud-to-cloud migrations and reverse cloud migrations.

On-premise to Cloud: Infrastructure, Database, Development platforms and Application everything candidate for cloud migrations.

Cloud-to-Cloud migration: Scenario among customers that want to avoid vendor lock-in and adopt a multi-cloud strategy.

Reverse cloud migration: a scenario where you’re looking to migrate off the cloud. It cloud be due to regulatory or some offline working needs.

Key Benefits of cloud migrations

Migrating to the cloud is a complex process that enables tremendous potential for your business. But complications created by inflexible legacy systems and vendor-driven lock-in can drain your investments and slow down transformation.

A cloud migration brings about various opportunities beyond cost saving. Modernization, scalability, agility, security, and compliance are some key know benefits. What you might not consider while defining your migration strategy are the added benefits like improved business agility, operational excellence, enhanced performance efficiency, automations, and geographical expansions that also come when migrating your data and analytics systems to the cloud.

Furthermore, modernization helps your business to empower DevOps, artificial intelligence, machine learning, real-time processing, and more.

Cloud migration strategies

There are countless possible architectures of cloud migrations beginning with three hosting options, IaaS, PaaS & SaaS, and 100s of various technologies, below are common strategies helps customer planning migration. But ultimately your migration largely depends on your current state and how your data and analytics platforms are proposed for future state.

Retain (Referred to as re-visit.) – Do nothing for now, keep on-premise. Examples for such applications are too much business risk, compliance, regulatory requirements, and less shelf life.

Retire  – Remove applications that are no longer needed.

Re-host (Referred to as a “lift and shift.”) – The lift and shift migration approach migrates your application and associated data to the cloud with minimal or no architecture changes.

Re-platform (Referred to as “lift, tinker, and shift.”) – Make cloud optimizations to achieve a tangible benefit. You will not change the core architecture of the application. An example of this is replacing database backends of applications with a corresponding PaaS database solution of a cloud provider.

Re-factor / Re-architect  – Re-architecting usually leads to the highest transformation costs and is mostly done for critical applications that need modernization, like when moving on-premise analytics to a real-time analytics cloud native solution.

Re-purchase  – Move from perpetual licenses to a software-as-a-service model. Examples for such is moving CPU based licence to “Pay as you go”.

Remember that no migration will ever look the same, but with Infocepts we have the proven expertise, frameworks, and technologies to help we narrow down your problems, develop pragmatic roadmaps, and provide accelerated solutions to achieve your objectives.

Get started today with Infocepts and start your journey towards modernization.

Recent Blogs

To unlock new opportunities, improve agility and accelerate innovation, enterprises are moving their business applications to cloud. The challenge is how to do it properly. The goal is reduce any disruptions in daily operations, and assure both current and future business and technology needs are met. Further, there are concerns about choosing the right architecture, avoiding vendor lock-ins, compliance with security standards, avoiding cost overruns and complexities of application re-platforming.

A successful seamless data and analytics migration to the cloud requires a well-defined, structured approach to planning and execution.

Evaluating Key Factors for the Cloud Migration Process

Planning is key for successful migrations, some critical factors need to be evaluated:

1. Network and security

Network first, ‘security’ next, everything else follows, before any other component, consider network design. This includes data connectivity between Data Centres (DC) and cloud to ensure seamless connectivity among enterprise infrastructure components.

2. Current architecture

Every enterprise that tries cloud migration will have several architectures that appear equally promising. It may further happen that all these options appear to check all the right boxes, thus clouding the decision further. In such scenarios, a POC becomes imperative.

3. Migration and maintenance costs

Total cost of ownership (TCO) is the sum of all costs involved in the purchase, operation, and maintenance of a given asset during its lifetime which reflects in time frame of 3-5 years. Migration to cloud may not bring cost-savings at all or the savings may not be realized upfront.

4. Stakeholder communication

Facilitate buy-in from your stakeholders through a multi-year TCO view, managing conflict of interest between business and IT.

5. Desired migration timeline

Migration to cloud is impacted by and impacts typical enterprise initiatives, visualizations, data center changes, platform upgrades, and major transformation.

When to Avoid Cloud Migrations

Despite of all high value there may be certain applications and process still need to be on-premise due to following:

  • Security and Compliance: Security and Compliance applications based on local regulatory requirements still considered to managed on-premise for better control
  • Low Latency Applications: Low latency applications need to be redesign and rearchitect as apart of migration, Example, Applications running on mainframes cannot be migrated to the cloud without modernizing or refactoring for the cloud.
  • Proprietary Hardware Platforms: Applications like Oracle Exadata, AWS S3 and Oracle Google Anthos, Amazon S3 running on engineered systems that cannot be re- hosted.
  • Offline applications: Applications dependent on a physical machine (e.g.: Scanner requiring a specific MAC address, App requiring specialized hardware drivers that cannot be virtualized, Apps requiring to read biometrics) or applications running on ship, remote mines where internet is not available all time.
  • Sunset Application: Applications which will be decommissioned in few years or near feature, as the TCO cost will not be realised before application discontinued

Pitfalls With Data and Analytics Leading to Failure

Based on industries best experience following could be possible reason(s) for cloud migration failures

  • Expectation mismatch: Set right expectation with stakeholders is very critical.
    Example: Cloud lift and shift just move your infrastructure to cloud but any slowness due to complex logic or business process will remain even after migration.
  • Skill Gaps : One of the biggest reason’s organizations delay moving into the cloud is that they lack IT personnel trained in cloud technology. When it comes to the cloud, business leaders are concerned about not having the expertise to handle the cloud migration and ongoing management of their cloud infrastructure.
  • Incorrect Choices : Create candidate architectures for future state involving choice of cloud vendor, technologies, and tools. Compare alternative architectures & agree on the best fit per the success criteria specified in the define phase
  • Change Resistance : Change resistance another salient challenge which exists in many organizations but not visible upfront, cloud brings lot of cultural shift in organisation where skill transformations, opportunities and legacy ethos need to be changed.
  • Org Misalignment : No common consensus building between business and IT stakeholders, other Platform transformation/upgrade in progress while cloud migration needs better synchronisation and alignments.

Tools and Services for a Successful Cloud migration

Cloud migration tools help determine costs, capacity, and prerequisites of your cloud landscape configuration before the team embarks on your migration journey. Below is a selection of tools that are commonly used during a migration and adoption readiness assessment to help foster a successful migration.

Cloud Service Provider (CSP) – Tools

These tools are specific to service providers and mostly assessment results are biased towards offering provided by vendors, some common examples as below:

Managed Service Provider (MSP)- Solutions and Frameworks

These solutions and frameworks provide the best combination of all cloud offerings and are tailored for your organization. A product agnostic solution framework will not only provide you with a product assessment but will provide an end to end roadmap of transformations.

Our Infocepts Cloud Migration Solution leverages our proven framework for cloud strategy, assessment, and road mapping to craft an execution-focused, holistic cloud strategy. In addition, Infocepts cloud migration accelerators provide expedited, error free and cost-effective migration approach with low risk and planning.

Your cloud migration is just the beginning of your journey. Expected TCO require continuous rationalization, monitoring, service operation to achieve your strategic goals.

With Infocepts you realize the intended benefits without disruption, and with no surprises in your cloud cost bill. Get started today on your cloud migration journey!

Recent Blogs