A collection of information technology and consulting knowledge
Category: Business Intelligence
Business intelligence (BI) is a technology-driven process for analyzing data and presenting actionable information to help executives, managers and other corporate end users make informed business decisions.
data-driven decision making is at the center of all things. The emergence of
data science and machine learning has further reinforced the importance of data
as the most critical commodity in today’s world. From FAAMG (the biggest five
tech companies: Facebook, Amazon, Apple, Microsoft, and Google) to governments
and non-profits, everyone is busy leveraging the power of data to achieve final
goals. Unfortunately, this growing demand for data has exposed the inefficiency
of the current systems to support the ever-growing data needs. This
inefficiency is what led to the evolution of what we today know as Logical Data
What Is a Logical
simple words, a data lake is a data repository that is capable of storing any
data in its original format. As opposed to traditional data sources that
use the ETL (Extract, Transform, and Load) strategy, data lakes work on the ELT
(Extract, Load, and Transform) strategy. This means data does not have to be
first transformed and then loaded, which essentially translates into reduced
time and efforts. Logical data lakes have captured the attention of
millions as they do away with the need to integrate data from different data
repositories. Thus, with this open access to data, companies can now begin to
draw correlations between separate data entities and use this exercise to their
Primary Use Case
Scenarios of Data Lakes
Logical data lakes are a
relatively new concept, and thus, readers can benefit from some knowledge of
how logical data lakes can be used in real-life scenarios.
Experimental Analysis of Data:
Logical data lakes can
play an essential role in the experimental analysis of data to establish its
value. Since data lakes work on the ELT strategy, they grant deftness and speed
to processes during such experiments.
To store and
analyze IoT Data:
Logical data lakes can
efficiently store the Internet of Things type of data. Data lakes are capable
of storing both relational as well as non-relational data. Under logical data
lakes, it is not mandatory to define the structure or schema of the data
stored. Moreover, logical data lakes can run analytics on IoT data and come up
with ways to enhance quality and reduce operational cost.
To improve Customer
Logical data lakes can
methodically combine CRM data with social media analytics to give businesses an
understanding of customer behavior as well as customer churn and its various
To create a Data
Logical data lakes
contain raw data. Data warehouses, on the other hand, store structured and
filtered data. Creating a data lake is the first step in the process of data
warehouse creation. A data lake may also be used to augment a data warehouse.
reporting and analytical function:
Data lakes can also be
used to support the reporting and analytical function in organizations. By
storing maximum data in a single repository, logical data lakes make it easier
to analyze all data to come up with relevant and valuable findings.
A logical data lake is a comparatively new area of study. However, it can be said with certainty that logical data lakes will revolutionize the traditional data theories.
The 360-degree view of
the consumer is a well-explored concept, but it is not adequate in the digital
age. Every firm, whether it is Google or Amazon, is deploying tools to
understand customers in a bid to serve them better. A 360-degree view demanded
that a company consults its internal data to segment customers and create
marketing strategies. It has become imperative for companies to look outside
their channels, to platforms like social media and reviews to gain insight into
the motivations of their customers. The 720-degree view of the customer is
further discussed below.
What is the
720-degree view of the customer?
A 720-degree view of the customer refers to a
three-dimensional understanding of customers, based on deep analytics. It
includes information on every customer’s level of influence, buying behavior,
needs, and patterns. A 720-degree view will enable retailers to offer relevant
products and experiences and to predict future behavior. If done right, this
concept should assist retailers leverage on emerging technologies, mobile
commerce, social media, and cloud-based services, and analytics to sustain
lifelong customer relationships
What Does a
720-Degree View of the Customer Entail?
Every business desires to cut costs, gain an
edge over its competitors, and grow their customer base. So how exactly will a
720-degree view of the customer help a firm advance its cause?
Social media channels help retailers interact
more effectively and deeply with their customers. It offers reliable insights
into what customers would appreciate in products, services, and marketing
campaigns. Retailers can not only evaluate feedback, but they can also deliver
real-time customer service. A business that integrates its services with social
media will be able to assess customer behavior through tools like dislikes and
likes. Some platforms also enable customers to buy products directly.
Customer analytics will construct more detailed customer profiles by
integrating different data sources like demographics, transactional data, and
location. When this internal data is added to information from external
channels like social media, the result is a comprehensive view of the customer’s
needs and wants. A firm will subsequently implement more-informed decisions on
inventory, supply chain management, pricing, marketing, customer segmentation,
and marketing. Analytics further come in handy when monitoring transactions,
personalized services, waiting times, website performance.
The modern customer demands convenience and
device compatibility. Mobile commerce also accounts for a significant amount of
retail sales, and retailers can explore multi-channel shopping experiences. By
leveraging a 720-degree view of every customer, firms can provide consumers
with the personalized experiences and flexibility they want. Marketing
campaigns will also be very targeted as they will be based on the transactional
behaviors of customers. Mobile commerce can take the form of mobile
applications for secure payment systems, targeted messaging, and push
notifications to inform consumers of special offers. The goal should be to
provide differentiated shopper analytics.
Cloud-based solutions provide real-time data across multiple channels, which illustrates an enhanced of the customer. Real-time analytics influence decision-making in retail and they also harmonize the physical and retail digital environments. The management will be empowered to detect sales trends as transactions take place.
The Importance of
the 720-Degree Customer View
Traditional marketers were all about marketing
to groups of similar individuals, which is often termed as segmentation. This technique
is, however, giving way to the more effective concept of personalized
marketing. Marketing is currently channeled through a host of platforms,
including social media, affiliate marketing, pay-per-click, and mobile. The
modern marketer has to integrate the information from all these sources and
match them to a real name and address. Companies can no longer depend on a
fragmented view of the customer, as there has to be an emphasis on
personalization. A 720-degree customer view can offer benefits like:
Firms can improve customer acquisition by
depending on the segment differences revealed from a new database of customer
intelligence. Consumer analytics will expose any opportunities to be taken
advantage of while external data sources will reveal competitor tactics. There
are always segment opportunities in any market, which are best revealed by
real-time consumer data.
Marketers who rely on enhanced digital data can
contribute to cost management in a firm. It takes less investment to serve
loyal and satisfied consumers because a firm is directing addressing their
needs. Technology can be used to set customized pricing goals and to segment
New Products and
Real-time data, in addition to third-party information, have a crucial impact on pricing. Only firms with a robust and relevant competitor and customer analytics and data can take advantage of this importance. Marketers with a 720-degree view of the consumer across many channels will be able to utilize opportunities for new products and personalized pricing to support business growth
The first 360 degrees include an enterprise-wide
and timely view of all consumer interactions with the firm. The other 360
degrees consists of the customer’s relevant online interactions, which
supplements the internal data a company holds. The modern customer is making
their buying decisions online, and it is where purchasing decisions are
influenced. Can you predict a surge in demand before your competitors? A
720-degree view will help you anticipate trends while monitoring the current
View and Big Data
Firms are always trying to make decision making
as accurate as possible, and this is being made more accessible by Big Data and
analytics. To deliver customer-centric experiences, businesses require a
720-degree view of every customer collected with the help of in-depth analysis.
Big Data analytical capabilities enable monitoring
of after-sales service-associated processes and the effective management of
technology for customer satisfaction. A firm invested in being in front of the
curve should maintain relevant databases of external and internal data with
global smart meters. Designing specific products to various segments is made
easier with the use of Big Data analytics. The analytics will also improve
asset utilization and fault prediction. Big Data helps a company maintain a
clearly-defined roadmap for growth
It is the dream of every enterprise to tap into
customer behavior and create a rich profile for each customer. The importance
of personalized customer experiences cannot be understated in the digital era.
The objective remains to develop products that can be advertised and delivered
to customers who want them, via their preferred platforms, and at a lower
Data virtualization is a data management approach that allows retrieving and manipulation of data without requiring technical data details like where the data is physically located or how the data is formatted at the source. Denodo is a data virtualization platform that offers more use cases than those supported by many data virtualization products available today. The platform supports a variety of operational, big data, web integration, and typical data management use cases helpful to technical and business teams. By offering real-time access to comprehensive information, Denodo helps businesses across industries execute complex processes efficiently. Here are 10 Denodo data virtualization use cases.
1. Big data analytics
Denodo is a popular data virtualization tool for examining large data sets to uncover hidden patterns, market trends, and unknown correlations, among other analytical information that can help in making informed decisions.
2. Mainstream business intelligence and data warehousing
Denodo can collect corporate data from external data sources and operational systems to allow data consolidation, analysis as well as reporting to present actionable information to executives for better decision making. In this use case, the tool can offer real-time reporting, logical data warehouse, hybrid data virtualization, data warehouse extension, among many other related applications.
3. Data discovery
Denodo can also be used for self-service business intelligence and reporting as well as “What If” analytics.
4. Agile application development
Data services requiring software development where requirements and solutions keep evolving via the collaborative effort of different teams and end-users can also benefit from Denodo. Examples include Agile service-oriented architecture and BPM (business process management) development, Agile portal & collaboration development as well as Agile mobile & cloud application development.
5. Data abstraction for modernization and migration
Denodo also comes in handy when reducing big data sets to allow for data migration and modernizations. Specific applications for this use case include, but aren’t limited to data consolidation processes in mergers and acquisitions, legacy application modernization and data migration to the cloud.
6. B2B data services & integration
Denodo also supports big data services for business partners. The platform can integrate data via web automation.
7. Cloud, web and B2B integration
Denodo can also be used in social media integration, competitive BI, web extraction, cloud application integration, cloud data services, and B2B integration via web automation.
8. Data management & data services infrastructure
Denodo can be used for unified data governance, providing a canonical view of data, enterprise data services, virtual MDM, and enterprise business data glossary.
9. Single view application
The platform can also be used for call centers, product catalogs, and vertical-specific data applications.
10. Agile business intelligence
Last but not least, Denodo can be used in business intelligence projects to improve inefficiencies of traditional business intelligence. The platform can develop methodologies that enhance outcomes of business intelligence initiatives. Denodo can help businesses adapt to ever-changing business needs. Agile business intelligence ensures business intelligence teams and managers make better decisions in shorter periods.
With over two decades of innovation, applications in 35+ industries and multiple use cases discussed above, it’s clear why Denodo a leading platform in data virtualization.
Today, newfound efficiencies and innovation are key to any business success – small, medium or large. In the rapidly evolving field of data analytics, innovative approaches to handling data are particularly important since data is the most valuable resource any business can have. IBM common SQL Engine is delivering application and query compatibility that is allowing companies to turn their data into actionable insights. This is allowing businesses to unleash the power of their databases without constraints.
But, is this really important?
Yes. Many businesses have accumulated tons of data over the years. This data resides in higher volumes, more locations throughout an enterprise – on-premise and on-cloud –, and in greater variety. Typically, this data should be a huge advantage, providing enterprises with actionable insights. But, often, this doesn’t happen.
IBM Hybrid Data Management.
With such a massive barrel of complex legacy data, many organizations find it confusing to decide what to do with it. Or where to start. The process of migrating all that data into new systems is simply a non-starter. As a solution, enterprises are turning to IBM Db2 – a hybrid, intuitive data approach that marries data and analytics seamlessly. IBM Db2 hybrid data management allows flexible cloud and on-premises deployment of data.
However, such levels of flexibility typically require organizations to rewrite or restructure their queries, and applications that will use the diverse, ever-changing data. These changes may even require you to license new software. This is costly and unfeasible. To bridge this gap, the Common SQL Engine (CSE) comes into play.
How IBM Common SQL Engine is Positioning Db2 for the Future?
The IBM Common SQL Engine inserts a single layer of data abstraction at the very data source. This means that, instead of migrating the data all at once, you can now apply data analytics wherever the data resides – whether on private, public or hybrid cloud – by using the Common SQL Engine as a bridge.
The IBM’s Common SQL Engine provides portability and consistency of SQL commands, meaning that the SQL is functionally portable across multiple implementations. It allows seamless movement of workloads to the cloud and allows for multiplatform integration and configurations regardless of their programming language.
Ideally, the Common SQL Engine is supposed to be the heart of the query and the foundation of application compatibility. But it does so much more!
Its compatibility extends beyond data analytic applications to include security, management, governance, data management, and other functionalities as well.
How does this improve the quality, flexibility, and portability of Db2?
By allowing for integration across multiple platforms, workloads and programming languages, the Common SQL Engine, ultimately, leads to a “data without limits” environment for Db2 hybrid data management family through:
Query and application compatibility
The Common SQL engine (CSE) ensures that users can write a query, and be confident that it will work across the Db2 hybrid data management family of offerings. With the CSE, you can change your data infrastructure and location – on-cloud or on-premises – without having to worry about license costs and application compatibility.
Data virtualization and Integration
The common SQL engine has a built-in data virtualization service that ensures that you can access your data from all your sources. These services position Db2 family of offerings including, IBM Db2 warehouse, IBM Db2, IBM Db2 BigSQL amongst others.
This services also applies to IBM Integrated Analytics System, Teradata, Oracle, Puredata and Microsoft SQL server. Besides, you can work seamlessly with open-source solutions such as HIVE; and cloud sources such as Amazon Redshift. Such levels of integration are unprecedented!
By allowing users to effectively pull data from Db2 data stores and integrate it with data from non-IBM stores using a single query, the common SQL engine places Db2 at an authoritative position as compared to other data stores.
Licensing is one of the hardest nuts to crack, especially for smart organizations who rely on technologies such as the cloud to deliver their services. While application compatibility and data integration will save you time, flexible licensing saves you money, on the spot.
IBM’s common SQL engine allows flexible licensing, meaning that you can purchase one license model and deploy it whenever needed, or as your data architecture evolves. Using IBM’s FlexPoint licensing, you can purchase FlexPoints and use them across all Db2 data management offerings. This is a convenience in one place.
The flexible licensing will not only simplify the adoption and exchange of platform capabilities, but it also positions your business strategically by making it more agile. Your data managers will be able to access the tools needed on the fly, without going through a lethargic and tedious procurement process.
IBM Db2 Data Management Family Is Supported by Common SQL Engine (CSE) .
IBM Db2 is a family of custom, deployable database that allows enterprises to leverage existing investments. IBM Db2 allows businesses to use any type of data from an either structured or unstructured database (or data warehouse). It provides the right data foundation/environment with industry-leading data compression, on-premise and cloud deployment options, modern data security, robust performance for mixed loads and the ability to adjust and scale without redesigning.
The IBM Db2 family enable businesses to adapt, scale quickly and remain competitive without compromising security, risk levels or privacy. It features:
Deployment and flexibility: On-premises, scale-on demand, and private or cloud deployments• Compression and performance
Embedded IoT technology is allowing businesses to act fast on the fly.
Some of these Db2 family offerings that are supported by the common SQL engine include:
Db2 Big SQL
Db2 on Cloud
Db2 Warehouse on Cloud
IBM Integrated Analytics System (IIAS)
Db2 Family Offerings and Beyond
Since the common SQL engine mainly focuses on data federation and propensity, other non-IBM databases can as well plug into the engine for SQL processing. These other 3rd party offerings include:
Watson Data Platform
Microsoft SQL Server
IBM Common SQL engine is allowing organizations to fully use data analytics to future-proof their business, and as well remain agile and competitive. In fact, besides the benefits of having robust tools woven into CSE, this SQL engine offers superior analytics and machine-learning positioning. Data processing can now happen at the speed of light –- 2X to 5X faster. The IBM Common SQL engine adds important capabilities to Db2, including freedom of location, freedom of use, and freedom of assembly.
Globally, organizations are facing challenges emanating from data issues, including data consolidation, value, heterogeneity, and quality. At the same time, they have to deal with the aspect of Big Data. In other words, consolidating, organizing, and realizing the value of data in an organization has been a challenge over the years. To overcome these challenges, a series of strategies have been devised. For instance, organizations are actively leveraging on methods such as Data Warehouses, Data Marts, and Data Stores to meet their data assets requirements. Unfortunately, the time and resources required to deliver value using these legacy methods is a distressing issue. In most cases, typical Data Warehouses applied for business intelligence (BI) rely on batch processing to consolidate and present data assets. This traditional approach is affected by the latency of information.
As the name suggests, Big Data describes a large volume of data that can either be structured or unstructured. It originates from business processes among other sources. Presently, artificial intelligence, mobile technology, social media, and the Internet of Things (IoT) have become new sources of vast amounts of data. In Big Data, the organization and consolidation matter more than the volume of the data. Ultimately, big data can be analyzed to generate insights that can be crucial in strategic decision making for a business.
Features of Big Data
The term Big Data is relatively new. However, the process of collecting and preserving vast amounts of information for different purposes has been there for decades. Big Data gained momentum recently with the three V’s features that include volume, velocity, and variety.
Volume: First, businesses gather information from a set of sources, such as social media, day-to-day operations, machine to machine data, weblogs, sensors, and so on. Traditionally, storing the data was a challenge. However, the requirement has been made possible by new technologies such as Hadoop.
Velocity: Another defining nature of Big Data is that it flows at an unprecedented rate that requires real-time processing. Organizations are gathering information from RFID tags, sensors, and other objects that need timely processing of data torrents.
Variety: In modern enterprises, information comes in different formats. For instance, a firm can gather numeric and structured data from traditional databases as well as unstructured emails, video, audio, business transactions, and texts.
Complexity: As mentioned above, Big Data comes from diverse sources and in varying formats. In effect, it becomes a challenge to consolidate, match, link, cleanse, or modify this data across an organizational system. Unfortunately, Big Data opportunities can only be explored when an organization successfully correlates relationships and connects multiple data sets to prevent it from spiraling out of control.
Variability: Big Data can have inconsistent flows within periodic peaks. For instance, in social media, a topic can be trending, which can tremendously increase collected data. Variability is also common while dealing with unstructured data.
Big Data Potential and Importance
The vast amount of data collected and preserved on a global scale will keep growing. This fact implies that there is more potential to generate crucial insights from this information. Unfortunately, due to various issues, only a small fraction of this data actually gets analyzed. There is a significant and untapped potential that businesses can explore to make proper and beneficial use of this information.
Analyzing Big Data allows businesses to make timely and effective decisions using raw data. In reality, organizations can gather data from diverse sources and process it to develop insights that can aid in reducing operational costs, production time, innovating new products, and making smarter decisions. Such benefits can be achieved when enterprises combine Big Data with analytic techniques, such as text analytics, predictive analytics, machine learning, natural language processing, data mining and so on.
Big Data Application Areas
Practically, Big Data can be used in nearly all industries. In the financial sector, a significant amount of data is gathered from diverse sources, which requires banks and insurance companies to innovate ways to manage Big Data. This industry aims at understanding and satisfying their customers while meeting regulatory compliance and preventing fraud. In effect, banks can exploit Big Data using advanced analytics to generate insights required to make smart decisions.
In the education sector, Big Data can be employed to make vital improvements on school systems, quality of education and curriculums. For instance, Big Data can be analyzed to assess students’ progress and to design support systems for professors and tutors.
Healthcare providers, on the other hand, collect patients’ records and design various treatment plans. In the healthcare sector, practitioners and service providers are required to offer accurate and timely treatment that is transparent to meet the stringent regulations in the industry and to enhance the quality of life. In this case, Big Data can be managed to uncover insights that can be used to improve the quality of service.
Governments and different authorities can apply analytics to Big Data to create the understanding required to manage social utilities and to develop solutions necessary to solve common problems, such as city congestion, crime, and drug use. However, governments must also consider other issues such as privacy and confidentiality while dealing with Big Data.
In manufacturing and processing, Big Data offers insights that stakeholders can use to efficiently use raw materials to output quality products. Manufacturers can perform analytics on big data to generate ideas that can be used to increase market share, enhance safety, minimize wastage, and solve other challenges faster.
In the retail sector, companies rely heavily on customer loyalty to maintain market share in a highly competitive market. In this case, managing big data can help retailers to understand the best methods to utilize in marketing their products to existing and potential consumers, and also to sustain relationships.
Challenges Handling Big Data
With the introduction of Big Data, the challenge of consolidating and creating value on data assets becomes magnified. Today, organizations are expected to handle increased data velocity, variety, and volume. It is now a business necessity to deal with traditional enterprise data and Big Data. Traditional relational databases are suitable for storing, processing, and managing low-latency data. Big Data has increased volume, variety, and velocity, making it difficult for legacy database systems to efficiently handle it.
Failing to act on this challenge implies that enterprises cannot tap the opportunities presented by data generated from diverse sources, such as machine sensors, weblogs, social media, and so on. On the contrary, organizations that will explore Big Data capabilities amidst its challenges will remain competitive. It is necessary for businesses to integrate diverse systems with Big Data platforms in a meaningful manner, as heterogeneity of data environments continue to increase.
Virtualization involves turning physical computing resources, such as databases and servers into multiple systems. The concept consists of making the function of an IT resource simulated in software, making it identical to the corresponding physical object. Virtualization technique uses abstraction to create a software application to appear and operate like hardware to provide a series of benefits ranging from flexibility, scalability, performance, and reliability.
Typically, virtualization is made possible using virtual machines (VMs) implemented in microprocessors with necessary hardware support and OS-level implementations to enhance computational productivity. VMs offers additional convenience, security, and integrity with little resource overhead.
Benefits of Virtualization
Achieving the economics of wide-scale functional virtualization using available technologies is easy to improve reliability by employing virtualization offered by cloud service providers on fully redundant and standby basis. Traditionally, organizations would deploy several services to operate at a fraction of their capacity to meet increased processing and storage demands. These requirements resulted in increased operating costs and inefficiencies. With the introduction of virtualization, the software can be used to simulate functionalities of hardware. In effect, businesses can outstandingly eliminate the possibility of system failures. At the same time, the technology significantly reduces capital expense components of IT budgets. In future, more resources will be spent on operating, than acquisition expenses. Company funds will be channeled to service providers instead of purchasing expensive equipment and hiring local personnel.
Overall, virtualization enables IT functions across business divisions and industries to be performed more efficiently, flexibly, inexpensively, and productively. The technology meaningfully eliminates expensive traditional implementations.
Apart from reducing capital and operating costs for organizations, virtualization minimizes and eliminates downtime. It also increases IT productivity, responsiveness, and agility. The technology provides faster provisioning of resources and applications. In case of incidents, virtualization allows fast disaster recovery that maintains business continuity.
Types of Virtualization
There are various types of virtualization, such as a server, network, and desktop virtualization.
In server virtualization, more than one operating system runs on a single physical server to increase IT efficiency, reduce costs, achieve timely workload deployment, improve availability and enhance performance.
Network virtualization involves reproducing a physical network to allow applications to run on a virtual system. This type of virtualization provides operational benefits and hardware independence.
In desktop virtualization, desktops and applications are virtualized and delivered to different divisions and branches in a company. Desktop virtualization supports outsourced, offshore, and mobile workers who can access simulate desktop on tablets and iPads.
Characteristics of Virtualization
Some of the features of virtualization that support the efficiency and performance of the technology include:
Partitioning: In virtualization, several applications, database systems, and operating systems are supported by a single physical system since the technology allows partitioning of limited IT resources.
Isolation: Virtual machines can be isolated from the physical systems hosting them. In effect, if a single virtual instance breaks down, the other machine, as well as the host hardware components, will not be affected.
Encapsulation: A virtual machine can be presented as a single file while abstracting other features. This makes it possible for users to identify the VM based on a role it plays.
Data Virtualization – A Solution for Big Data Challenges
Virtualization can be viewed as a strategy that helps derive information value when needed. The technology can be used to add a level of efficiency that makes big data applications a reality. To enjoy the benefits of big data, organizations need to abstract data from different reinforcements. In other words, virtualization can be deployed to provide partitioning, encapsulation, and isolation that abstracts the complexities of Big Data stores to make it easy to integrate data from multiple stores with other data from systems used in an enterprise.
Virtualization enables ease of access to Big Data. The two technologies can be combined and configured using the software. As a result, the approach makes it possible to present an extensive collection of disassociated and structured and unstructured data ranging from application and weblogs, operating system configuration, network flows, security events, to storage metrics.
Virtualization improves storage and analysis capabilities on Big Data. As mentioned earlier, the current traditional relational databases are incapable of addressing growing needs inherent to Big Data. Today, there is an increase in special purpose applications for processing varied and unstructured big data. The tools can be used to extract value from Big Data efficiently while minimizing unnecessary data replication. Virtualization tools also make it possible for enterprises to access numerous data sources by integrating them with legacy relational data centers, data warehouses, and other files that can be used in business intelligence. Ultimately, companies can deploy virtualization to achieve a reliable way to handle complexity, volume, and heterogeneity of information collected from diverse sources. The integrated solutions will also meet other business needs for near-real-time information processing and agility.
In conclusion, it is evident that the value of Big Data comes from processing information gathered from diverse sources in an enterprise. Virtualizing big data offers numerous benefits that cannot be realized while using physical infrastructure and traditional database systems. It provides simplification of Big Data infrastructure that reduces operational costs and time to results. Shortly, Big Data use cares will shift from theoretical possibilities to multiple use patterns that feature powerful analytics and affordable archival of vast datasets. Virtualization will be crucial in exploiting Big Data presented as abstracted data services.
Today, a business heavily depends on data to gain insights into their processes and operations and to develop new ways to increase market share and profits. In most cases, data required to generate the insights are sourced and located in diverse places, which requires reliable access mechanism. Currently, data warehousing and data virtualization are two principal techniques used to store and access the sources of critical data in a company. Each approach offers various capabilities and can be deployed for particular use cases as described in this article.
A data warehouse is designed and developed to secure host historical data from different sources. In effect, this technique protects data sources from performance degradation caused by the impact of sophisticated analytics and enormous demands for reports. Today, various tools and platforms have been developed for data warehouse automation in companies. They can be deployed to quicken development, automate testing, maintenance, and other steps involved in data warehousing. In a data warehouse, data is stored as a series of snapshots, where a record represents data at a particular time. In effect, companies can analyze data warehouse snapshots to compare data between different periods. The results are converted into insights required to make crucial business decisions.
Moreover, a data warehouse is optimized for other functions, such as data retrieval. The technology duplicates data to allow database de-normalization that enhances query performance. The solution is further deployed to create an enterprise data warehouse (EDW) used to service the entire organization.
Features of a Data Warehouse
A data warehouse is subject-oriented, and it is designed to help entities analyze data. For instance, a company can start a data warehouse focused on sales to learn more about sales data. Analytics on this warehouse can help establish insights such as the best customer for the period. The data warehouse is subject oriented since it can be defined based on a subject matter.
A data warehouse is integrated. Data from various sources is first out into a consistent format. The process requires the firm to resolve some challenges, such as naming conflicts and inconsistencies on units of measure.
A data warehouse in nonvolatile. In effect, data entered into the warehouse should not change after it is stored. This feature increases accuracy and integrity in data warehousing.
A data warehouse is time variant since it focuses on data changes over time. Data warehousing discovers trends in business by using large amounts of historical data. In effect, a typical operation in a data warehouse scans millions of rows to return an output.
A data warehouse is designed and developed to handle ad hoc queries. In most cases, organizations may not predict the amount of workload of a data warehouse. Therefore, it is recommendable to optimize the data warehouse to perform optimally over any possible query operation.
A data warehouse is regularly updated by the ETL process using bulk data modification techniques. Therefore, end users cannot directly update the data warehouse.
Advantages of Data Warehousing
The primary motivation for developing a data warehouse is to provide timely information required for decision making in an organization. A business intelligence data warehouse serves as an initial checkpoint for crucial business data. When a company stores its data in a data warehouse, tracking it becomes natural. The technology allows users to perform quick searches to be able to retrieve and analyze static data.
Another driver for companies investing in data warehouses involves integrating data from disparate sources. This capability adds value to operational applications like customer relationship management systems. A well-integrated warehouse allows the solution to translate information to a more usable and straightforward format, making it easy for users to understand the business data.
The technology also allows organizations to perform a series of analysis on data.
A data warehouse reduces the cost to access historical data in an organization.
Data warehousing provides standardization of data across an organization. Moreover, it helps identify and eliminate errors. Before loading data, the solution shows inconsistencies to users and corrects them.
A data warehouse also improves the turnaround time for analysis and report generation.
The technology makes it easy for users to access and share data. A user can conduct a quick search on a data warehouse to find and analyze static data without wasting time.
Data warehousing removes informational processing load from transaction-oriented databases.
Disadvantages of Data Warehousing
While data warehousing technology is undoubtedly beneficial to many organizations, not all data warehouses are relevant to a business. In some cases, a data warehouse can be expensive to scale and maintain.
Preparing a data warehouse is time-consuming since it requires users to input raw data, which has to be achieved manually.
A data warehouse is not a perfect choice for handing unstructured and complex raw data. Moreover, it faces difficulties incompatibility. Depending on the data sources, companies may require a business intelligence team to ensure compatibility is achieved for data coming from sources running distinct operating systems and programs.
The technology requires a maintenance cost to continue working correctly. The solution needs to be updated with latest features that might be costly. Regularly maintaining a data warehouse will need a business to spend more on top of the initial investment.
A data warehouse use can be limited due to information privacy and confidentiality issues. In most cases, businesses collect and store sensitive data belonging to their clients. Viewing it is only allowed to individual employees, which limits the benefits offered by a data warehouse.
Data Warehousing Use Case
There are a series of ways organizations use data warehouses. Businesses can optimize the technology for performance by identifying the type of data warehouse they have.
A data warehouses can be used by an organization that is struggling to report efficiently on business operations and activities. The solution makes it possible to access the required data
A data warehouse is necessary for an organization where data is copied separately by different divisions for analysis in spreadsheets that are not consistent with one another.
Data warehousing is crucial in organizations where uncertainties about data accuracy are causing executives to question the veracity of reports.
A data warehouse is crucial for business intelligence acceleration. The technology delivers rapid data insights to analysts at different scales, concurrency, and without requiring manual tuning or optimization of a database.
Data virtualization technology does not require transfer or storage of data. Instead, users employ a combination of application programming interfaces (APIs) and metadata (data about data) to interface with data in different sources. Users use joined queries to gain access to the original data sources. In other words, data virtualization offers a simplified and integrated view to business data in real-time as requested by business users, applications, and analytics. In effect, the technology makes it possible to integrate data from distinct sources, formats, and locations, without replication. It creates a unified virtual data layer that delivers data services to support users and various business applications.
Data virtualization performs many of the same data integration functions, that is, extract, transform, and load, data replication, and federation. It leverages modern technology to deliver real-time data integration with agility, low cost, and high speed. In effect, data virtualization eliminates traditional data integration and reduces the need for replicated data warehouses and data marts in most cases.
Capabilities and Benefits of Data Virtualization
There are various benefits of implementing data virtualization in an organization.
Firstly, data virtualization allows access and leverage of all information that helps a firm achieve a competitive advantage. The solution offers a unified virtual layer that abstracts the underlying source complexity and presents disparate data sources as a single source.
Data virtualization is cheaper since it does not require actual hardware devices to be installed. In other words, organizations no longer need to purchase and dedicate a lot of IT resources and additional monetary investment to create on-site resources, similar to the one used in a data warehouse.
Data virtualization allows speedy deployment of resources. In this solution, resource provisioning is fast and straightforward. Organizations are not required to set up physical machines or to create local networks or install other IT components. Users have a single point of access to a virtual environment that can be distributed to the entire company.
Data virtualization is an energy-efficient system since the solution does not require additional local hardware and software. Therefore, an organization will not be required to install cooling systems.
Disadvantages of Data Virtualization
Data virtualization creates a security risk. In the modern world, having information is a cheap way to make money. In effect, company data is frequently targeted by hackers. Implementing data virtualization from disparate sources may give an opportunity to malicious users to steal critical information and use it for monetary gain.
Data virtualization requires a series of channels or links that must work in cohesion to perform the intended task. In this cases, all data sources should be available for virtualization to work effectively.
Data Virtualization Use Cases
Companies that rely on business intelligence require data virtualization for rapid prototyping to meet immediate business needs. Data virtualization can create a real-time reporting solution that unifies access to multiple internal databases.
Provisioning data services for single-view applications, such as in customer service and call center applications require data virtualization.
Machine learning is Artificial Intelligence (AI) which enables a system to learn from data rather than through explicit programming. Machine learning uses algorithms that iteratively learn from data to improve, describe data, and predict outcomes. As the algorithms ingest training data to produce a more precise machine learning model. Once trained, the machine learning model, when provided data will generate predictions based on the data that taught the model. Machine learning is a crucial ingredient for creating modern analytics models.