Using Logical Data Lakes

Today, 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 Lakes.

What Is a Logical Data Lake?

In 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 advantage.

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.

To conduct 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 Interaction:

  • 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 causes.

To create a Data Warehouse:

  • 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.

To support 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.

Related References

A 720-Degree View of the Customer

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

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


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.

Mobile Commerce

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

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:

Customer Acquisition

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.

Cutting Costs

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 customers effectively.

New Products and Pricing

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

Advance Customer Engagement

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 ones.

720-degree Customer 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

Conclusion

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 cost. 

What is a Private Cloud?

The private cloud concept is running the cloud software architecture and, possibly specialized hardware, within a companies’ own facilities and support by the customer’s own employees, rather than having it hosted from a data center operated by commercial providers like Amazon, IBM Microsoft, or Oracle.

A companies’ private (internal) cloud may be a one or more of these patterns and may be part of a larger hybrid-cloud strategy.

  • Home-Grown, where the company has built its own software and or hardware could infrastructure where the private could is managed entirely by the companies’ resources. 
  • Commercial-Off-The-Self (COTS), where the cloud software and or hardware is purchased from a commercial vendor and install in the companies promises where is it is primarily managed by the companies’ resources with licensed technical support from the vendor.
  • Appliance-Centric, where vendor specialty hardware and software are pre-assembled and pre-optimized, usually on proprietary databases to support a specific cloud strategic.
  • Hybrid-Cloud, which may use some or all of the about approaches and have added components such as:
    • Virtualization software to integrate, private-cloud, public-cloud, and non-cloud information resources into a central delivery architecture.
    • Public/Private cloud where proprietary and customer sensitive information is kept on promise and less sensitive information is housed in one or more public clouds. The Public/Private hybrid-cloud strategy can also be provision temporary short duration increases in computational resources or where application and information development occur in the private cloud and migrated to a public cloud for productionalization.

In the modern technological era, there are a variety of cloud patterns, but this explanation highlights the major aspects of the private cloud concept which should clarify and assist in strategizing for your enterprise cloud.

Related References

What is Development Operations (DevOps)?

With modern businesses continually looking for ways to streamline their operations, DevOps has become a common approach to software delivery used by development and operation teams to set up, test, deploy, and assess applications.

To help you understand more about this approach, let’s briefly discuss DevOps.

What is DevOps?

DevOps comes from two words- ‘development and operations.’ It describes a set of IT practices, which seeks to have software developers and operations team work together on the same project in a more collaborative and free-flowing way.

In simple words, this is a culture that promotes cooperation between Development and Operations teams in an organization to ensure faster production in an automated, recurring manner.

The approach aims at breaking down traditional barriers that have existed between these two important teams of the IT department in any organization. When deployed smoothly, this approach can help reduce time and friction that occur when deploying new software applications in an organization.

These efforts lead to quicker development cycles, which ultimately save money and time, and give an organization a competitive edge against its rivals with longer, more ridged development cycles.

DevOps helps to increase the speed with which an organization delivers applications and services to customers, thereby competing favorably and actively in the market.

What Is Needed for DevOps to Be Successful Executed?

For an organization to appeal to customers, it must be agile, lean, and swift to respond to dynamic demands in the market.  For this to happen, all stakeholders in the delivery process have to work together.

Development teams, which focus on designing, developing, delivering, and running the software reliably and quickly, need to work with the operations team, which is tasked with the work of identifying and resolving problems in the software as soon as possible.

By having a common approach across software developers and operation teams, an organization will be able to monitor and analyze holdups and scale as quickly as possible. This way, they will be able to deliver and deploy reliable software in a shorter time.

We hope that our simplified guide has enabled you to understand what DevOps is and why it is important in modern organizations.

What is cloud computing? 

Cloud computing is a service driven model for enabling ubiquitous, convenient, on demand network access to a shared pool computing resources that can be rapidly provisioned and released with minimal administrative effort or service provider interaction.

Related References

Technical Debt

During the software engineering process, there are different issues which should be dealt with or else they will subject the project to unnecessary costs later. The technical debt perspective should be considered in each step of software development. For instance, when analyzing the cost of cloud approaches, you need to take into consideration the technical debt. You should as well factor the engineering aspect when making technical decisions such as choosing between cloud services vs. homegrown solutions.

What is technical debt?

Technical debt refers to the implied cost which will be incurred to do additional rework on a system after the engineering process is done. For example, engineers can choose to go for an easy option so that they can save time during the product design. The right steps which they will avoid will later need to be implemented which will mean a product has to be recalled or it will have to be fixed after it has reached the market which will cost more in terms of resources and manpower.

What are the most common types/causes technical debts?

Deliberate tech debt

In this case, engineers are aware of the step which is necessary during project implementation, but they will ignore it provided they can go for a shortcut which will save on cost and avail the product to the market. For instance, when analyzing the advantage of using the public cloud, some engineers may assume certain benefits, and later they will realize they are very necessary hence they are forced to go back and procure the system. It will lead to wastage in the company. Some engineers will not like doing the same process every now and then; they can avoid a given process only to expose the final product to flaws which will require re-engineering.

Accidental/outdated design tech debt

After designing a product or software, with time the technology will advance and render the design less effective in solving certain needs. For instance, due to advancement in technology, the tools you incorporated in a given software may end up being flawed which will make the product less effective which may necessitate re-engineering. Engineers may try their level best to come up with great designs, but advancement in technology can make their designs less effective.

Bit rot tech debt

It is a situation where a complexity develops over time. For example, a system or a component can develop unnecessary complexity due to different changes which have been incorporated over time. As engineers try to solve emerging needs, they can end up exposing the product to more complications which can be costly in the long run.

Strategies for minimizing technical debt

How to minimize deliberate tech debt

To avoid the tech debt, you need to track the backlog when engineers started the work. If you can track the backlog and identify areas where the engineers are trying to save time, you can avoid the debt.

Minimizing Accidental/outdated design tech debt

You need to refactor the subsystem every now and then so that you can identify the technical debt and fix it. For example, if the software is exposing you to unnecessary slowdowns, you need to fix the errors and make it meet industry standards.

Addressing Bit rot tech debt

Engineers should take time to understand the system they are running and clear any bad codes.

What Is Machine Learning?

Machine Learning
Machine Learning

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.