Customers have become even more dependent on online and mobile apps as quarantines and social distancing practices mandated during the Coronavirus are driving increased digital engagements. It’s only a matter of time before the demand for more advanced services will become a standard request for all financial services customers.
This will move the open banking trend forward in the US, despite the fact that unlike Europe and in Canada, open banking regulations don’t seem to be on the radar in the near future. Applying a broad-based standard to enforce APIs and data sharing for over 10,000 financial institutions is technically and logistically challenging.
Fear of Infection Drives Digital Banking
The Coronavirus has resulted in the massive closing of branch banks. New York City based Citigroup temporarily closed 100 branches and JP Morgan Chase, the largest bank in the country, announced plans to close about 1,000 branches. Those branches that are open will have reduced hours for receiving customers.
Approximately one-third of retail banking customers plan to increase their use of online and mobile banking services post COVID-19 as more services, such as loan and mortgage requests and payments, are becoming virtual and digital on-boarding is available for new accounts. Mobile payment applications are shooting up in popularity as people are reluctant to touch cash or credit cards. These numbers are expected to rise as the pandemic continues to spread, and especially if there is a second wave resulting in another round of restrictions and closures.
However, there is more to digital banking than replacing services that were previously provided in branch banks. Open banking can power new innovative digital services to give banks a competitive advantage by using standard APIs to capture data from customers’ accounts.
For example, with access to a comprehensive view of a customer’s data, banks can allow payments from accounts held elsewhere. Digital personal advisors powered by machine learning based applications can analyze all of the savings and investment accounts and customer’s personal goals to recommend optimal opportunities.
Financial firms can also use data provided to identify the most relevant service for a customer, the most competitive service pricing, and the most advantageous terms and conditions for investments, loans and mortgages based on advanced risk models and market offerings. Just think how easy it could be to convince customers to move to your bank by showing them how you can improve their earnings or reduce their expenses.
These services are also not limited to B2C. The Bank of America has recently unveiled digital tools designed for small enterprises that help streamline transactions and generate cash-flow projections along with easy connectivity with financial consultants for quick advice.
Towards a More Efficient System Architecture
As more and more people and businesses rely on digital apps for their banking services, the number of online transactions continue to grow; putting a strain on existing IT computing resources. The massive increase in the number of queries is resulting in bottlenecks that can degrade the performance of applications and affect customer service levels.
When customers wait too long to complete a transaction or receive approval for a loan, or if they understand that they can receive better conditions from another bank, they are more likely to switch. Thus, banks are faced with the need to scale up their expensive legacy infrastructure to provide the expected user quality of experience, or to find modern solutions that can elastically scale to manage this data at the required speeds, with an optimized TCO.
In many cases large financial services organizations are limited by tangled and archaic systems that are too complex to optimally manage, process and analyze their huge amounts of data from different sources.
This was revealed recently in a BIAN survey where over 60 percent of respondents expressed concerns that banks will struggle to open up their APIs because of the “current state of banks’ core architecture.”
There are however, innovations designed to help banks process more data, more cost effectively without the need to rip and replace existing platforms. For example, some in-memory computing platforms can co-locate all data models (structured, unstructured and semi-structured), service business logic and analytics in an in-memory data core and distribute it across a horizontally scalable architecture for exceptionally fast processing times, while eliminating the need to invest in excess capacity.
These modern data and analytics platforms support agile development and deployment of applications that involve interactive queries, analytics and even machine learning running simultaneously on mutable streaming, transactional, and historical data that is stored on external data lakes and data warehouses.
Applications are able to access data on external data lakes whether they are on cloud, on premise or hybrid 100 times faster which not only speeds up queries and BI reporting, but also improves the accuracy of time critical applications and decision making services, such as risk analysis, fraud detection, loan approvals, dynamic pricing and personalized services.
Digital Banking Will Become the New Norm
Digital banking will become the new norm. However, financial services will need to adopt new technologies to leverage and monetize their data to maintain their market leadership.
The full potential for digital banking can only be realized if there is a high-performance data management infrastructure in place that can enable queries and analytics to be completed quickly and reliably for time critical applications at an optimized TCO. COVID-19 may have resulted in closed branches, but it can accelerate the adoption of open banking, to bring convenience and new applications that will change the way customers do their banking.
Join Hands with Instagram’s New Algorithm to Boost Your Business’s User Engagement
👉 Most people are not at all happy with the latest 📸 Instagram algorithm. However, if you want to make the most of your business, you should understand how it works. The trick here is you must work with its algorithm and not go against it here; it is easy for your know-how. 👇🔻
🚩This post will guide you on how the 🆕 new Instagram algorithm works and how you can use it for the advantage of your business-
How does the new Instagram algorithm work?
The new 📸 Instagram ♀️ algorithm is a mystery to many users on the platform. It no longer functions at a slow pace as it did in the past. To score high on Instagram today, you need to be extremely diligent as a business owner. The algorithm needs to be fully optimized so that you get success in the platform. So, read and remember the following factors to help you determine how your posts can perform well to boost user engagement and sales revenue for your business with success-
⦁ The volume of social engagement you receive -: Note, your posts with the maximum number of shares, views, comments, etc., will rank high in the Instagram feed over others. When your post receives a lot of comments and likes, the Instagram platform gets a signal that it is engaging to users and has high-quality content. It wants more users to see it, and the algorithm on the platform will display it to other users online.
However, here again, there is a catch. It is not only the engagement that Instagram considers now; it is how fast your Instagram post engages the readers in some cases. Trending hashtags on Instagram, for instance, is the best-known example of the above. The volume of engagement you get for your business posts is important; however, how quickly you get this engagement is even more important!
👉▶Tip- You should discover when the best time of the day to post is. In this way, you can schedule posts targeted at that time only. You should post when most users can see it. This increases your chances of boosting engagement faster, and the Instagram algorithm as it knows what somebody likes on Instagram and will take over from here to share it with those users who will ✌ ⚡ like, share and comment on your post.
⦁ How long are people looking at your post on Instagram -: If you see the algorithm that Facebook uses, it takes into account how long users look at your post and spend time interacting with its content. Instagram is no different! Its algorithm will take a look at the duration of time that users spend on your post too. This is again, another important factor that Instagram uses to boost posts.
👉▶Tip- Here, you should craft a great 📸 Instagram caption for your post to boost user engagement. If your Instagram caption is engaging, users will want to read it or click on the button that says “more” to spend more time on your post, and in this way, you can boost more engagement with success on 📸 Instagram.
This is why Boomerangs and videos are generally posted in the 📹 video format. This makes them perform well with the Instagram algorithm. Users take more time to watch them toll the end. Another great way for you to make users stay on your posts is to swipe up CTA action for them to view more. This is another great strategy you can use for your business to boost engagement.
⦁ When did you post your photo?-: This is another factor that influences how the 📸 Instagram algorithm works for your business is determining the time of the post. It depends on how often you use the 📲 app. In the past, the algorithm used to give you insights into the recent posts; however, if you tend to log on to Instagram just a few times in one week, it will show posts that have been posted recently. You might even get likes from a previous post that you published a few days ago. The target here is to keep users updated on the posts they might have missed due to the fact that they did not log in regularly.
👉 This means that users can see your posts now for longer periods.
⦁ The sort of content you post also influences engagement – Think about if 📸 Instagram only focused on content with the optimal engagement; users would only see that content every time they logged into 📸 Instagram. However, this is not the case with the Instagram algorithm.
For instance, the content 👥 users’ genre searches for in the platform also influence the algorithm of how Instagram works. If a user is a fan of sports, says the NBA and sees that genre of content regularly, Instagram will immediately catch on to this and bring the user more similar content related to sports and the NBA. It knows that the user will be interested in such posts and perhaps bring news and updates of the LA Lakers to boost 👤 user engagement and satisfaction.
⦁ Accounts that users search for -: Like the above, the accounts that users search for also determines how the 📸 Instagram algorithm works. This is why when users search a specific 🧾 account many times; Instagram will bring more such content from other accounts to that user. This is why they see it often in their Instagram feeds.
From the above, it is evident that if you want to work with the new Instagram algorithm, you must understand how it works and optimize your posts to help it boost your business. In the past, the feed on Instagram was chronological; however, things have changed now.
🟥 So, ensure that your CTA is strong; you use the right hashtags, post at the best time, and make your feed on Instagram as attractive as possible. In this way, you can boost user engagement, lead conversions, sales, and of course, gain a strategic edge in the market as well.
↘ Source: Ron Johnson. Ron is a Marketer. He always shares his tips on trendy marketing tricks. He always implements new tricks in his field.
Top 10 Big Data trends of 2020
During the last few decades, Big Data has become an insightful idea in all the significant technical terms. Additionally, the accessibility of wireless connections and different advances have facilitated the analysis of large data sets. Organizations and huge companies are picking up strength consistently by improving their data analytics and platforms.
2019 was a major year over the big data landscape. In the wake of beginning the year with the Cloudera and Hortonworks merger, we’ve seen huge upticks in Big Data use across the world, with organizations running to embrace the significance of data operations and orchestration to their business success. The big data industry is presently worth $189 Billion, an expansion of $20 Billion more than 2018, and is set to proceed with its rapid growth and reach $247 Billion by 2022.
It’s the ideal opportunity for us to look at Big Data trends for 2020.
Chief Data Officers (CDOs) will be the Center of Attraction
The positions of Data Scientists and Chief Data Officers (CDOs) are modestly new, anyway, the prerequisite for these experts on the work is currently high. As the volume of data continues developing, the requirement for data professionals additionally arrives at a specific limit of business requirements.
CDO is a C-level authority at risk for data availability, integrity, and security in a company. As more businessmen comprehend the noteworthiness of this job, enlisting a CDO is transforming into the norm. The prerequisite for these experts will stay to be in big data trends for quite a long time.
Investment in Big Data Analytics
Analytics gives an upper hand to organizations. Gartner is foreseeing that organizations that aren’t putting intensely in analytics by the end of 2020 may not be ready to go in 2021. (It is expected that private ventures, for example, self-employed handymen, gardeners, and many artists, are excluded from this forecast.)
The real-time speech analytics market has seen its previously sustained adoption cycle beginning in 2019. The idea of customer journey analytics is anticipated to grow consistently, with the objective of improving enterprise productivity and the client experience. Real-time speech analytics and customer journey analytics will increase its popularity in 2020.
Multi-cloud and Hybrid are Setting Deep Roots
As cloud-based advances keep on developing, organizations are progressively liable to want a spot in the cloud. Notwithstanding, the process of moving your data integration and preparation from an on-premises solution to the cloud is more confounded and tedious than most care to concede. Additionally, to relocate huge amounts of existing data, organizations should match up to their data sources and platforms for a little while to months before the shift is complete.
In 2020, we hope to see later adopters arrive at a conclusion of having multi-cloud deployment, bringing the hybrid and multi-cloud philosophy to the front line of data ecosystem strategies.
Actionable Data will Grow
Another development concerning big data trends 2020 recognized to be actionable data for faster processing. This data indicates the missing connection between business prepositions and big data. As it was referred before, big data in itself is futile without assessment since it is unreasonably stunning, multi-organized, and voluminous. As opposed to big data patterns, ordinarily relying upon Hadoop and NoSQL databases to look at data in the clump mode, speedy data mulls over planning continuous streams.
Because of this data stream handling, data can be separated immediately, within a brief period in only a single millisecond. This conveys more value to companies that can make business decisions and start processes all the more immediately when data is cleaned up.
Continuous Intelligence is a framework that has integrated real-time analytics with business operations. It measures recorded and current data to give decision-making automation or decision-making support. Continuous intelligence uses several technologies such as optimization, business rule management, event stream processing, augmented analytics, and machine learning. It suggests activities dependent on both historical and real-time data.
Gartner predicts more than 50% of new business systems will utilize continuous intelligence by 2022. This move has begun, and numerous companies will fuse continuous intelligence during 2020 to pick up or keep up a serious edge.
Machine Learning will Continue to be in Focus
Being a significant innovation in big data trends 2020, machine learning (ML) is another development expected to affect our future fundamentally. ML is a rapidly developing advancement that used to expand regular activities and business processes
ML projects have gotten the most investments in 2019, stood out from all other AI systems joined. Automated ML tools help in making pieces of knowledge that would be difficult to separate by various methods, even by expert analysts. This big data innovation stack gives faster results and lifts both general productivity and response times.
Abandon Hadoop for Spark and Databricks
Since showing up in the market, Hadoop has been criticized by numerous individuals in the network for its multifaceted nature. Spark and managed Spark solutions like Databricks are the “new and glossy” player and have accordingly been picking up a foothold as data science workers consider them to be as an answer to all that they disdain about Hadoop.
However, running a Spark or Databricks work in data science sandbox and then promoting it into full production will keep on facing challenges. Data engineers will keep on requiring more fit and finish for Spark with regards to enterprise-class data operations and orchestration. Most importantly there are a ton of options to consider between the two platforms, and companies will benefit themselves from that decision for favored abilities and economic worth.
In-memory computing has the additional advantage of helping business clients (counting banks, retailers, and utilities) to identify patterns rapidly and break down huge amounts of data without any problem. The dropping of costs for memory is a major factor in the growing enthusiasm for in-memory computing innovation.
In-memory innovation is utilized to perform complex data analyses in real time. It permits its clients to work with huge data sets with a lot more prominent agility. In 2020, in-memory computing will pick up fame because of the decreases in expenses of memory.
IoT and Big Data
There are such enormous numbers of advancements that expect to change the current business situations in 2020. It is hard to be aware of all that, however, IoT and digital gadgets are required to get a balance in big data trends 2020.
The function of IoT in healthcare can be seen today, likewise, the innovation joining with gig data is pushing companies to get better outcomes. It is expected that 42% of companies that have IoT solutions in progress or IoT creation in progress are expecting to use digitized portables within the following three years.
Digital Transformation Will Be a Key Component
Digital transformation goes together with the Internet of Things (IoT), artificial intelligence (AI), machine learning and big data. With IoT connected devices expected to arrive at a stunning 75 billion devices in 2025 from 26.7 billion presently, it’s easy to see where that big data is originating from. Digital transformation as IoT, IaaS, AI and machine learning is taking care of big data and pushing it to regions inconceivable in mankind’s history.
What are the differences between Data Lake and Data Warehouse?
- Understand the meaning of data lake and data warehouse
- We will see what are the key differences between Data Warehouse and Data Lake
- Understand which one is better for the organization
From processing to storing, every aspect of data has become important for an organization just due to the sheer volume of data we produce in this era. When it comes to storing big data you might have come across the terms with Data Lake and Data Warehouse. These are the 2 most popular options for storing big data.
Having been in the data industry for a long time, I can vouch for the fact that a data warehouse and data lake are 2 different things. Yet I see many people using them interchangeably. As a data engineer understanding data lake and data warehouse along with its differences and usage are very crucial as then only will you understand if data lake fits your organization or data warehouse?
So in this article, let satiate your curiosity by explaining what data lake and warehousing are and highlight the difference between them.
Table of Contents
- What is a Data Lake?
- What is a Data Warehouse?
- What are the differences between Data Lake and Data Warehouse?
- Which one to use?
What is a Data Lake?
A Data Lake is a common repository that is capable to store a huge amount of data without maintaining any specified structure of the data. You can store data whose purpose may or may not yet be defined. Its purposes include- building dashboards, machine learning, or real-time analytics.
Now, when you store a huge amount of data at a single place from multiple sources, it is important that it should be in a usable form. It should have some rules and regulations so as to maintain data security and data accessibility.
Otherwise, only the team who designed the data lake knows how to access a particular type of data. Without proper information, it would be very difficult to distinguish between the data you want and the data you are retrieving. So it is important that your data lake does not turn into a data swamp.
Image Source: here
What is a Data Warehouse?
A Data Warehouse is another database that only stores the pre-processed data. Here the structure of the data is well-defined, optimized for SQL queries, and ready to be used for analytics purposes. Some of the other names of the Data Warehouse are Business Intelligence Solution and Decision Support System.
What are the differences between Data Lake and Data Warehouse?
|Data Lake||Data Warehouse|
|Data Storage and Quality||The Data Lake captures all types of data like structure, unstructured in their raw format. It contains the data which might be useful in some current use-case and also that is likely to be used in the future.||It contains only high-quality data that is already pre-processed and ready to be used by the team.|
|Purpose||The purpose of the Data Lake is not fixed. Sometimes organizations have a future use-case in mind. Its general uses include data discovery, user profiling, and machine learning.||The data warehouse has data that has already been designed for some use-case. Its uses include Business Intelligence, Visualizations, and Batch Reporting.|
|Users||Data Scientists use data lakes to find out the patterns and useful information that can help businesses.||Business Analysts use data warehouses to create visualizations and reports.|
|Pricing||It is comparatively low-cost storage as we do not give much attention to storing in the structured format.||Storing data is a bit costlier and also a time-consuming process.|
Which one to use?
We have seen what are the differences between a data lake and a data warehouse. Now, we will see which one should we use?
If your organization deals with healthcare or social media, the data you capture will be mostly unstructured (documents, images). The volume of structured data is very less. So here, the data lake is a good fit as it can handle both types of data and will give more flexibility for analysis.
If your online business is divided into multiple pillars, you obviously want to get summarized dashboards of all of them. The data warehouses will be helpful in this case in making informed decisions. It will maintain the data quality, consistency, and accuracy of the data.
Most of the time organizations use a combination of both. They do the data exploration and analysis over the data lake and move the rich data to the data warehouses for quick and advance reporting.
In this article, we have seen the differences between data lake and data warehouse on the basis of data storage, purpose to use, which one to use. Understanding this concept will help the big data engineer choose the right data storage mechanism and thus optimize the cost and processes of the organization.
The following are some additional data engineering resources that I strongly recommend you go through-
If you find this article informative, then please share it with your friends and comment below your queries and feedback.
cogu joins MIBR as manager and coach
Strategic Resources Files Mustavaara Technical Report
Ur-Energy Announces Extension of State Bond Loan and Provides Update
Pettit Marine Paint Develops the Most Effective Anti-fouling Paint to Hit the Market in Many Years – ODYSSEY® TRITON
Core Lab Reports Third Quarter 2020 Results From Continuing Operations:
A Difference-Making Disinfectant
How Car Tires Are Manufactured
5 Real World Applications of the Doppler Effect
Join Hands with Instagram’s New Algorithm to Boost Your Business’s User Engagement
BioMicrobics Acclaimed by Frost & Sullivan for Its Continuous Innovation-led Growth in the Water and Wastewater Treatment Market
SME Education Foundation Seeks Industry Involvement for Unadilla Valley High School Initiative to Create STEM Opportunities for Students
Verisem Acquires State-of-the-Art Vegetable Seed Processing Facility, Further Enhancing Capabilities
Global Synthetic and Bio Based Polypropylene Market 2020-2026 Growing Demand in the Automotive Industries
AI-Driven Dynamic Filmmaking is the Future
Growing Concerns around Global Warming Are Set to Drive Hypercar Market Forward: TMR
Angry Birds VR and Acron: Attack of the Squirrels Gear up for Halloween
This Is a $103 Billion Profit Opportunity
Power Plant Boiler Market by Type, Capacity, Technology, Fuel Type, and Region – Global Forecast to 2025
Rising Phoenix Royalties Announces Second Yoakum County, Permian Basin, Oil and Natural Gas Royalty Acquisition
Chem-Dry Grows Amid Pandemic with Signed Agreements to Open 64 New Franchises Across the Nation
Key Trends and Recent Innovations in Powder Bed Fusion, IDTechEx Identifies
Bitcoin Breaks $12K Resistance and Aims for $14K as BTC Rallies Higher in the Expense of Altcoins
Pasternack Now Offers a Broad Selection of Field Replaceable Connectors Available for Same-Day Shipment
Star Wars: Tales from the Galaxy’s Edge Gameplay Trailer Drops With November Date for Oculus Quest
Central Banks Are Getting a Prototype of CBDC
The Briefing: RVShare raises over $100M, Google disputes charges, and more
Mode Adds Bitcoin to Reserves, Joining Microstrategy and Square
Bitcoin Breaks the $12,000 Overhead Resistance; Here’s Why
Has Bitcoin met its match with this altcoin?
Syte Sees $30M Series C For Product Discovery
What could a Democrat Sweep in US Elections mean for Bitcoin?
Yearn Finance Adds GUSD Vaults and Updated Keep3r Network Details
B2BX Exchange Announces B2BX Token Buyback
Coinend: 1, 2, 3 Take off -New Gamified Crypto Prediction Platform!
GenTech Proudly Secures Deal with TruLife Distribution to Drive Growth in SINFIT Digital Sales
Coinend: 1, 2, 3, Take off – New gamified crypto prediction platform!
PayPal to allow transactions in Bitcoin, Ethereum etc. from 2021
Ripple CTO Assesses XRP as a Bridge Cryptocurrency Between CBDCs, Stablecoins, and Fiat
PayPal Introduces Crypto Trading, Plans To Start Crypto Shopping Facility
Top 10 Big Data trends of 2020
Techcrunch5 days ago
Original Content podcast: It’s hard to resist the silliness of ‘Emily in Paris’
Gaming1 week ago
‘Call of Duty: Mobile’ Season 11 Anniversary Update Is Out Now with a New Battle Pass Coming Soon, New Maps, XP Card Changes, and a Lot More
AI1 week ago
Key Challenges and Benefits of AI Chatbots: A Balanced Perspective
Startups5 days ago
Solve the ‘dead equity’ problem with a longer founder vesting schedule
1 week ago
Filecoin Fervor Gaining Momentum in China as Mainnet Nears Launch
1 week ago
Bitfinex launches EOS, Litecoin, and Polkadot Perpetual Swaps
AI1 week ago
Future of Contact Centers Enabling Employees to Work from Home
Startups5 days ago
Three views on the future of media startups