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Big Data, Data Solutions, Healthcare, Retail
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Trends and Industries: How Data Solutions upend existing sectors to new heights in 2023?
Trends and Industries: How Data Solutions upend existing sectors to new heights in 2023? 650 486 Exist Software Labs

The defining era of data is currently upon us. Business model threats and economic shocks are common. Power is changing wherever you look, including in the market, our technological infrastructure, and the interactions between companies and customers. Change and disruption have become the norm. Data Solutions have been useful in innovating the industry.

Data-savvy businesses are well-positioned to triumph in a winner-take-all market. In the past two years, the distance between analytics leaders and laggards has increased. Higher revenues and profitability can be found in companies that have undergone digital transformation, embraced innovation and agility, and developed a data-fluent culture. Those who were late to the game and who still adhere to antiquated tech stacks are struggling, if they are even still in operation.

So, when you create your data and analytics goals for 2023, these are the key trends to help you stay one step ahead of your competitors.

Healthcare

Data Analytics and Data Solutions can be used to improve patient outcomes, streamline clinical trial processes, and reduce healthcare costs. 

Some specific examples of how Analytics is being used in healthcare include:

  1. Improving patient outcomes: Analytics can be used to identify patterns and trends in patient data that can help healthcare providers make more informed decisions about treatment plans. For example, data from electronic health records (EHRs) can be analyzed to identify risk factors for certain conditions, such as heart disease or diabetes, and to determine the most effective treatments for those conditions.
  2. Streamlining clinical trial processes: Data Analytics can be used to improve the efficiency of clinical trials by allowing researchers to identify suitable candidates more quickly and by helping them to track the progress of trials more closely.
  3. Reducing healthcare costs: Analytics can be used to identify inefficiencies in healthcare systems and to help providers implement cost-saving measures. For example, data analysis can be used to identify patterns of overutilization or unnecessary testing, and to develop strategies for reducing these costs.

Financial services

Data Analytics can be used to detect fraud, assess risk, and personalized financial products and services. 

Some specific examples of how Data Analytics is being used in the financial industry include:

  1. Fraud Detection: Data Analytics can be used to identify patterns and anomalies in financial transactions that may indicate fraudulent activity. This can help financial institutions to prevent losses due to fraud and to protect their customers.
  2. Risk Assessment: Analytics can be used to assess the risk associated with various financial products and services. For example, data analysis can be used to assess the creditworthiness of borrowers or to identify potential risks in investment portfolios.
  3. Personalizing financial products and services: Analytics can be used to gain a deeper understanding of individual customers and to personalize financial products and services accordingly. For example, data analysis can be used to identify the financial needs and preferences of individual customers, and to offer customized financial products and services that are tailored to those needs.

Retail

Retail companies can use Data Analytics to optimize pricing, understand customer behavior, and personalize marketing efforts. 

Some specific examples of how Data Analytics is being used in the retail industry include:

  1. Prizing Optimization: Retail companies can use Data Analytics to identify patterns in customer behavior and to optimize their pricing strategies accordingly. For example, data analysis can determine the most effective price points for different products and identify opportunities for dynamic pricing (i.e., adjusting prices in real time based on demand).
  2. Understanding customer behavior: Analytics can be used to gain a deeper understanding of customer behavior and preferences. This can help retailers to make more informed decisions about the products and services they offer, and to identify opportunities for cross-selling and upselling.
  3. Personalizing marketing efforts: Analytics can be used to deliver more personalized and targeted marketing efforts to customers. For example, data analysis can be used to identify customer segments with similar characteristics and to develop customized marketing campaigns for each segment.
  4. Cost Reduction: Being able to have a JIT (Just in Time) procurement and storage of items which in turn increases/optimizes warehouse capacity and reduces spoilage, and improves logistics.

Manufacturing

Data Analytics can be used to optimize supply chain management, improve production efficiency, and reduce costs. 

Some specific examples of how Data Analytics is being used in the manufacturing industry include:

  1. Optimizing supply chain management: Analytics can be used to improve the efficiency of the supply chain by identifying bottlenecks and inefficiencies, and by developing strategies to address these issues.
  2. Reducing fuel consumption: Analytics can be used to identify patterns in fuel consumption and to identify opportunities for fuel savings. For example, data analysis can be used to identify the most fuel-efficient routes or to identify vehicles that are consuming more fuel than expected.
  3. Improving fleet management: Analytics can be used to improve the efficiency of fleet management by identifying patterns in vehicle maintenance and repair data, and by helping fleet managers to develop strategies to optimize vehicle utilization and reduce downtime.
  4. Forecast roadworthiness of vehicles: This can help set trends on when a vehicle would break down or need repairs based on utilization, road conditions, climate, and driving patterns.

Energy

Data Analytics can be used to optimize the production and distribution of energy, as well as to improve the efficiency of energy-consuming devices.

Some specific examples of how Analytics is being used in the energy industry include:

  1. Optimizing the production and distribution of energy: Analytics can be used to optimize the production and distribution of energy by identifying patterns in energy demand and by developing strategies to match supply with demand. For example, data analysis can be used to predict when energy demand is likely to be highest and to adjust energy production accordingly.
  2. Improving the efficiency of energy-consuming devices: Analytics can be used to identify patterns in energy consumption and to identify opportunities for energy savings. For example, data analysis can be used to identify devices that are consuming more energy than expected and to develop strategies to optimize their energy use.
  3. Monitoring and optimizing energy systems: Analytics can be used to monitor and optimize the performance of energy systems, such as power plants and transmission grids. Data analysis can be used to identify potential problems or inefficiencies and to develop strategies to address them.

Agriculture

Analytics can be used to optimize crop yields, improve the efficiency of agricultural processes, and reduce waste.

Some specific examples of how Data Analytics is being used in agriculture include:

  1. Optimizing crop yields: Analytics can be used to identify patterns in crop growth and to develop strategies to optimize crop yields. For example, data analysis can be used to identify the most suitable locations for growing different crops and to develop customized fertilization and irrigation plans.
  2. Improving the efficiency of agricultural processes: Data Analytics can be used to identify patterns in agricultural data and to develop strategies to optimize processes such as planting, fertilizing, and harvesting.
  3. Waste Reduction: Analytics can be used to identify patterns in food waste and to develop strategies to reduce waste. For example, data analysis can be used to identify the most common causes of food waste on farms and to develop strategies to address those issues.

These are just a few examples of the many industries that are likely to adopt Data Analytics technologies as part of their digital transformation efforts in the coming years. 

Other industries that are also likely to adopt Analytics Technologies include Government, Education, and Media, among others. In general, Data Analytics Technologies are being adopted across a wide range of industries because they can help organizations to gain insights from their data, make more informed decisions, and improve their operations. 

As more and more organizations recognize the value of Analytics, it’s likely that we’ll see even greater adoption of these technologies in the coming years.

To learn more about our Data Solutions Services, click here.

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Befriending Your Data in 2021
Befriending Your Data in 2021 768 487 Exist Software Labs

It’s the new year and everybody is still living in the wake of the COVID-19 pandemic. We all need a friend in times of trouble and this is no different in the case of business organizations.

This year, 2021, the friend that your company needs more than ever, especially in these trying times, is data. Given the disruption that this virus caused in the preceding year, enterprises need to start (if they haven’t already) befriending their own internal data, and perhaps external data as well, if they are to at least stay viable and at most grow.

The following are some insights from respected data management leaders on how to make friends with your data this year:

  • Eran Vanounou, CEO at Varada

    “Data warehouses are not going to disappear. Data warehouses will continue to be an important legacy technology that organizations will use for mission-critical business application well into the future. With the transition to the cloud, data warehouses got a fresh new look and offer some modern attractive capabilities including self-service and serverless. With the rise of the cloud, data lakes are the new kid on the block. Data lakes are becoming a commodity, legacy technology in their own right. Their rapid emergence from the innovation stage means two things going forward.

    First, organizations will demand simpler, easier to manage, and more cost-effective means of extracting usable business intelligence from their data lakes, using as many data sources as possible. Second, those same organizations will want the above benefit to be delivered via tools that do not lock them into proprietary data management platforms. In short, 2021 will begin to see the rapid introduction and evolution of tools that allow users to keep their data lakes in one place and under their control while driving performance up and cost down.”

  • Paige Roberts, Open-Source Relations Manager at Vertica

    “Distributed analytical databases and affordable scalable storage are merging into a single new thing called either a unified analytics warehouse or a data lake house depending on who you’re talking to. Data lake vendors are scrambling to add ACID capabilities, improve SQL performance, add governance, resource management, security, lineage, all the things that data warehouse vendors have been perfecting for the last three or four decades. During the ten years, while data lake software has been coalescing, analytical databases have seen their benefits and added them to their existing stacks: unlimited scale, support for widely varied data types, fast ingestion of streaming data, schema-on-read, and machine learning capabilities. Just like a lot of things used to claim to be cloudy before they really were, some vendors will claim to be a unified analytics warehouse when they’ve just jammed the two architectures together into a complicated mess, but everyone is racing to make it happen for real. I think the data warehouse vendors have an unbeatable head start because building a solid, dependable analytical database like Vertica can take ten years or more alone. The data lake vendors have only been around about ten years, and are scrambling to play catch-up.”

  • Raj Verma, CEO at SingleStore

    “One single SQL query for all data workloads

    The way forward is based not only on automation, but also on how quickly and widely you can make your analytics accessible and shareable. Analytics gives you a clear direction of what your next steps should be to keep customers and employees happy, and even save lives. Managing your data is no longer a luxury, but a necessity–and determines how successful you or your company will be. If you can remove complexity or cost of managing data, you’ll be very effective. Ultimately, the winner of the space will take the complexity and cost out of data management, and workloads will be unified so you can write one single SQL query to manage and access all workloads across multiple data residencies.”

  • Prat Moghe, CEO at Cazena

    “Expect more enterprises to declare the battle between data lakes and data warehouses over in 2021 – and focus on driving outcomes and modernizing.

    Data warehouses can continue to support reporting and business intelligence, while modern cloud data lakes support all analytics, AI and ML enablement far more flexibly, scalably, and inexpensively than ever – so enterprises can go transform quickly.

    Cloud migrations and related cloud data lake implementations will get demonstrably faster and easier as DIY approaches are replaced by turnkey SaaS platforms. Such solutions will slash production cloud data lake deployment times from months to minutes, while controlling costs and providing the continuous operations, security and compliance, AI and ML enablement, and self-service access required for modern analytics initiatives. That means that migrations that used to take 9-12+ months are complete in a fraction of the time.”

  • Adi Paz, CEO at GigaSpaces Technologies

    “Co-locating analytics and operational data results in faster data processing to accelerate actionable insights and response times for time-sensitive applications such as dynamic pricing, hyper-personalized recommendations, real-time fraud and risk analysis, business process optimization, predictive maintenance, and more.

    To successfully deploy analytics and ML in production, a more efficient Data Architecture will be deployed, combining OLTP (CRM, ERP, billing, etc.) with OLAP (data lake, data warehouse, BI, etc.) systems with the ability to build the feature vector more quickly, and with more data for accurate, timely results.”

To summarize the various points made by these industry pundits:

1

SQL-driven data warehouses are here to stay and will continue to be the data analytics platform of choice for enterprises in the current year.

2

Data management platforms that integrate well with existing data lakes will dominate as opposed to platforms that focus on one or the other.

3

Data management platforms that have built-in AI/ML functionalities will dominate as well, as this eliminates the cost and complexity of separate AI/ML analytics platforms.

4

Data management platforms that are cloud-ready will also have an edge over those that are not.

Is there a data management platform that possesses all these qualities and has a proven track record in Fortune 500 companies?

Yes, there is. It’s called Greenplum. Read about it here.

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The Metallica of Master Data Management: TIBCO EBX
The Metallica of Master Data Management: TIBCO EBX 768 487 Exist Software Labs

In the world of heavy metal, Metallica is considered, arguably, as the G.O.A.T. Some may contest this claim and cite the forefathers, like Black Sabbath or Led Zeppelin, but the prevailing sentiment is that the ‘Tallica boys are at the top of the heap.

One of the key achievements of this band is that they put out the highest-grossing metal album of all time. Released in 1986, Master of Puppets is Metallica’s best-selling album (surpassing every other metal band in the world in terms of raw sales).

If Master of Puppets is Metallica’s magnum opus, then Master Data Management’s masterpiece is no other than TIBCO EBX.

But first…what is Master Data Management?

Master data management (MDM) is the initiative of an enterprise that is keen on having data work for them to create a single repository of all master data, reference data, and metadata in order to minimize, if not totally eliminate, data errors and redundancy in business processes.

An MDM solution would typically be an interplay of Data Quality, Data Integration, and Data Governance practices.

What’s in it for me with Master Data Management?

The provision of a single point of reference for business-critical information eliminates the costliness of data redundancies that occur when organizations rely on multiple versions of data that reside in departmental silos.

For example, MDM can ensure that when customer information changes, the Sales & Marketing Department will not reach out to unreachable or different entities, but will consistently have a single, latest, and accurate view of the customer upon which to target their efforts.

What are the Basic Steps to Master Data Management?

  1. Discover the relevant and pertinent data sources to be mastered in your enterprise.
  2. Acquire the data (Data Integration proper, ETL, streaming, etc.).
  3. Cleanse the data (Data Quality proper).
  4. Enrich the data with data from other data sources that are external to your enterprise but are useful (e.g. social media, websites, etc.).
  5. Match the data with other data and look/flag for duplication.
  6. Merge the data and select the most up-to-date version of the data.
  7. Relate the mastered data with other relatable data in the enterprise.
  8. Secure the mastered data (masking, user roles & privileges, etc.).
  9. Deliver the mastered data to the appropriate and intended consumers and stakeholders.
  10. Govern the mastered data and ensure that master data management becomes a secure, repeatable, sustainable, and value-generating key framework in the enterprise.

Why rock with TIBCO EBX?

First, a history lesson. TIBCO EBX was the result of the acquisition of Orchestra Networks, a leader in MDM, by TIBCO Software last 2018. This assimilation proved monumental as evidenced by TIBCO EBX’s rankings in Gartner’s evaluations:

As you can see, TIBCO EBX is among the Top 2 leaders in the Leader quadrant, alongside the very expensive Informatica.

In actual MDM use cases, however, TIBCO EBX ranked highest in 5 of 6:

The latest 2020 Gartner report on the MDM space pretty much tells the same story:

tibcomdmgartner2020

Again…why rock with TIBCO EBX?

ONE PLATFORM FOR ALL YOUR DATA MANAGEMENT NEEDS

With EBX software, you only need one platform to do the job of multiple products, including MDM, reference data management, product master data management, party master data management, data governance, and hierarchy management.

SUPPORT FOR ALL TYPES OF BUSINESS FUNCTIONS

Operational and analytical processes may be different, but they have one thing in common: data powers them all. Instead of managing these assets in multiple, separate applications, the EBX platform provides a single resource to govern and manage them, providing consistency and cohesion to processes across your organization.

SUPPORT FOR ALL LEVELS OF USERS

  • Business Users: Delivers an intuitive, self-service experience for your business teams. Users view, search, author, edit, and approve changes in a workflow-driven, collaborative interface.
  • Data Stewards: Helps data stewards easily discern the quality of their data and take action using powerful data governance, matching, profiling, cleansing, workflow monitoring, quality analytics, and audit trail capabilities.
  • Developers/Analysts: Supports building and adapting applications quickly, without long and costly development projects. Project teams have full control over data models, workflow models, business rules, UI configuration, and data services.

FLEXIBILITY AND AGILITY

Custom applications and purpose-built MDM solutions are hard to change, but EBX software is flexible and agile. It uses a unique what-you-model-is-what-you-get design approach, with fully configurable applications generated on-the-fly. Long, costly development projects are eliminated. And EBX software includes all the enterprise class capabilities you need to create data management applications including user interfaces for authoring and data stewardship, workflow, hierarchy management, and data integration tools.1

Is that all?

TIBCO EBX’s best-of-breed capabilities include:

DATA MODELING

What you model is what you get. The flexible data model supports any master domain and relationships as well as complex and simple forms of data.

COLLABORATIVE WORKFLOW

Collaborate with everyone who touches your data. Manage updates, oversee change requests, and provide approvals through a customizable workflow.

HIERARCHY MANAGEMENT

Support any type of hierarchy and create alternate hierarchies without duplication. Now it’s easy to visualize and maintain complex relationships.

VERSION CONTROL

Manage and connect every version of data—past, present, and future.

PLATFORM COMPATIBILITY

Integrate with multiple platforms on-premises or in the cloud. Works with a wide range of interfaces, application servers, databases, and infrastructures.

INSIGHT WITH DASHBOARDS AND KPIS

Track, analyze, and measure data quality and performance through EBX dashboards.2

How can I buy tickets to the next concert?

If you want to learn more about MDM and how TIBCO EBX can help your organization eliminate bad data, data silos, and poor data visibility, contact EXIST Software Labs today!

Keep rockin’!

 

 

 

Footnotes:
1 TIBCO EBX Datasheet
2 ibid.

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