The Future Of Data Science And Analytics

Today, an increasing number of organizations strive to make data-driven decisions. This has increased the demand for processes and professionals that help ensure data quality. Data science and the professionals that enable it are a perfect example of this.
 
A multidisciplinary process, data science combines technology, algorithm development, and data inference to solve analytically complex problems. In simple words, data science is all about using data in creative ways to generate business value.
 
It is important to keep in mind that data science is no magic and won’t fix all of your company’s problems. However, it can help you to make more accurate decisions and automate repetitive tasks and choices that need to be performed/made by teams.
 
Huge data volumes exist within organizations and to extract value from this dataset is critical to unlock its power. Data science can help to do that. From identifying opportunities to defining goals to encouraging the adoption of best practices and making better decision, data science offers immense value to businesses. So, how can businesses benefit from the use of data science? Let’s find out.
 

The Benefits of Data Science for Businesses

The core purpose of any business is to serve customers. The better you serve customers, the more satisfied they will be. This satisfaction generally results in increased revenue or growth of the business. Following are four ways data science can help businesses to lower costs and increase revenue.
 

1. Ensure Better Decisions Across the Board

One thing that all businesses need is reliable information, and data science can help provide that. To facilitate an improved decision-making process across the organization, data science communicates and demonstrates the value of company’s analytics. It does this by measuring, tracking, and recording the performance metrics, which allows your staff to maximize their analytics capabilities, resulting in improved decision making across the board.
 

2. Increased Adoption of Best Practices and Greater Focus on Issues That Matter by The Staff

One of major responsibilities of data science is familiarizing your staff with the analytics product of the organization. Data science helps the staff to succeed by showcasing how the system can be used effectively to derive insight and drive action. Once the capabilities of product are well-known to the staff, they can take the next step of addressing the major challenges facing the business.
 

3. Identify New Opportunities

Improving business operations is one of the main objectives of data science. By enabling them to pinpoint inconsistencies in existing systems and processes, data science helps businesses to iron out discrepancies and create new ways of doing things. This drives innovation, process improvement, and revenue streams.
 

4. Make Quantifiable, Data-Driven Decisions and Test Them

Businesses no longer need to take high stake risks as data science allows gathering and analyzing data from various channels. While making data-driven decisions and then implementing them is useful, businesses should implement decisions only after testing them. This is where data science can help. Data science tests your decisions by measuring the key metrics related to the decisions and quantifying their success.
 

Data Science: Possible Evolution and Challenges of an Emerging Field

Being a relatively new field, data science is growing rather quickly. However, this rapid growth brings with it many visible challenges and problems. What are these challenges and problems faced by data science and how is the field likely to evolve? We analyze that next.
 

The Challenges to Data Science

Open sources, many machine learning frameworks help boost the adoption of artificial intelligence (AI) technology. TensorFlow, PyTorch, and Theano are some of the popular deep learning tools. By leveraging these tools, businesses made progress in areas such as speech recognition, algorithms and systems, and new computer vision.
 
Today, machine learning (ML) is gaining traction as a discipline of engineering. Businesses are building more advanced ML models and involving more people than before in the modelling process. Often, a team working on a single ML model comprises of more than a dozen engineers. However, the most important role in the environment is not assumed by machine learning frameworks rather it is played by collaboration..
 

The Issue of Reproducibility in Machine Learning

In ML, team collaboration is broken, and this is perfectly epitomized by the reproducibility issue. At times, even the original authors have failed to train the same model and get similar outcomes. This may sound like nonsense, but this is exactly how the industry functions today.
 
Most surprised by this are the software engineers who are well-equipped to build reproducible processes and formalize production life cycle using Source Control Systems (like Git) for storing code, Continuous Integration (CI) for merging all engineers work several times a day and Continuous Delivery System to build a product from source code in a push of a button.
 
In software engineering, Version control systems such as Git are a fundamental part of any collaboration tool. Moreover, all collaboration activities in software engineering are based on Git, which include coding sharing, merging code from all contributors and building a single product (continuous delivery), and software deployment. Versioning is what’s missing in ML projects.
 
In an ML project, source code is managed by Git but data lives outside this system. The resulting code and data discrepancy are one of the root causes of the reproducibility issue. It is hard to imagine a reproducible ML project without a consistent source code and data versioning system, which begs the question: how should this issue be addressed?
 

The Dilemma of Centralized Vs. Decentralized Data Versioning

What is the traditional approach to data versioning? It is to store and version data in the cloud. A centralized approach, this forces data scientists to upload all the source code and data files in separate directories in the cloud, providing data files and source code versioning feature on top of it.
 
The experimenting term, which combines both data and code, rather than the data and code versioning term is what these systems prefer to use. A foundation for reproducible ML project and researches is what is created by these experimentation platforms.
 
In the centralized approach, code and data are tied together into experiment and made ready for reproducibility. However, this approach does not ensure the ideal environment for data scientists who are used to working on their laptops the same way software engineers prefer not to write code online. On the other hand, a decentralized approach aims to tie code and data together right on the laptops of the data scientists the same way as Git does.
 
The decentralized approach of versioning data and code in open source solution in the form of Version Control Systems (VCS) can provide the ability of version code and data in the machines of the data scientists and also have reproducibility features.
 

Predictions for How the Data Science Infrastructure is Likely to Evolve

Let’s start from the very beginning, when the first version control systems (VCS) were created in the 70s. This was when open source and proprietary enterprise systems co-existed together. The early 80s was when enterprise level version control systems (VCS) made the entry, driving progress in this area. These were the first VCS with binary file support, a web interface, file deltas, and disk space optimization. Additionally, they were the first VCS to be distributed.
 
However, free and open source VCS overtook the proprietary ones and disrupted this area constantly: CVS came in the 90s, SVN in 2000s, and Git were available from 2005. Open source, distributed VCS completely changed the paradigm and the VCS market. The market itself became distributed. Instead of a single VCS with many features, companies started using an ecosystem of services.
 
Now, a new type of version control systems for ML and data projects is emerging: data science platforms. Tech giants are building their own data science platforms: Uber Michelangelo, Microsoft Azure Machine Learning Experimentation Service. Also, there are a few companies building products: dominodatalab.com, h2o.ai.On the open source side, you can find studio.ml, dataversioncontrol.com.
 
These data science platforms manage code versions as well as data file versions. Combination of code and data version from an experiment. Experiment is a first-class citizen in the platforms and most of the platform features are related to the experiment management. Currently, most of these platforms are created for enterprises. Data science platforms include many features related not only to versioning, but also to the collaboration between data scientists and the deployment of the ML model, the same way as the old enterprise VCS included additional services.
 
In addition to the above, there are a few online platforms for data scientists such as flyodhub.com, Neptune.ML, and Coment.Ml. The idea and feature set are a bit different from the enterprise ones. However, the core function is the same—manage experiments as combination of code and data versions and provide some additional features like model serving.
 
Data science platforms might follow the same road as the VCS. So, what can we predict based on the VCS history?
 
First, and it is already clear, that enterprise level data science platforms are superior to open source analogs in terms of supported features and user adoption. Moreover, they include many features not directly related to versioning and this pattern quite evident today.
 
Second, and this might happen, enterprise level data science platforms have a pretty good chance to be disrupted by open source analogues. The key for disruption is a wider adoption. With the current speed of the progress, this might take a couple of years, not a decade as it previously took.
 

The Prediction for the Long Term

Here is an interesting question: Will we see in the future distributed data science platform and the distributed ecosystem where data scientists can work distributed on the same ML model, use on service for training the intermediate models, another service for collecting theirs models together into a single data product, 3rd service for visualization, 4th for collaboration and 5th for serving the result online? Considering the logic VCS history, this will inevitably happen.
 
However, to make the above a reality, a conceptually new type of system is needed. First, an underlying level of software should act as a binding force that connects all these different parts, just like Git connects all the software parts today. But, rather than decentralizing them, most of the systems tend to centralize everything in a single place. The only system that follows the decentralization approach is an open source dataversioncontrol.com or DVC.
 
DVC is decentralized from the very beginning. Even code and data services are separated and decentralized and only on a data scientist machine DVC ties code and data versions together to logically create an experiment. When two data scientists are working together on a project, they use a Git service (like GitHub) to communicate by code and the project meta information and a separate cloud storage (S3 or GCP storage) to communicate by data and ML model files.
 
With DVC, a data scientist can distribute his workflow between his laptop, a desktop with GPU and a remote machine for a more powerful GPU or more memory and then collect the best experiments and models into hit primary environment. Look complicated? Many common industrial software engineering workflows are even more complicated than this.
 
The DVC tool creators recently founded a startup http://iterative.ai in San Francisco to build next components of this distributed ecosystem - data scientists collaboration service and ML model execution service.
 
DVC might be the foundation to the ecosystem of distributed services for data scientists instead of large monolithic data scientist platforms. All these systems have significant overhead. However, since collaboration is tough, it pays back in large companies.
 
However, why should anyone spend 2-8 hours to copy-paste their code to cloud and retrain a model just to have a “collaborative” version? And why should files in their laptop be outside of data management lifecycle? Distributed Git approach should help.
 
Today, it is not clear which approach of data versioning will be adopted faster by the data science community: the centralized approach or the decentralized one. However, given the fact that users of these systems are accomplished technical personnel, we won’t be surprised if the most advanced technology wins the battle.
 

The Top Challenges for Data Analytics and Strategies to Overcome Them

A Gartner report emphasizes that to make analytics the heart of a business strategy requires understanding the benefits and risks of different types of analysis. Additionally, the report helps data and analytics leaders to identify analytics’ best practices and the vendors that will deliver maximum business impact.
 
In addition to knowing the best practices for data analytics, one must be aware of the top challenges for data analytics and the strategies to overcome them. Also important is to create new data and analytic roles in an organization that are fit for the future.
 
In another report, Gartner provides the must-have roles for data and analytics in 2018, This is applicable in 2020 as well. The must-have roles for data and analytics outlined in the Gartner report are Chief Data Officer, Data-Driven Facilitator, Data Analyst, Business Process Analyst, Data Engineer, Data Ethicist, Information Architect, Lead Information Steward and Information Steward, and Master Data Management (MDM) Program Manager.
 
Gartner came up with these roles after conducting a survey that identified the top 5 internal roadblocks to the success of the Office of the Chief Data Officer (CDO). The roadblocks included cultural challenge to accept change, poor data literacy, lack of relevant skills or staff, lack of focus in defining the most important initiatives, and lack of resources/funding to support programs.
 
Coming back to the most important challenges in data analytics, the four main barriers to effective data analytics are the same as the four main barriers to effective data management—Trust, Diversity, Complexity, and Literacy. However, when we look at things at a more granular level, we find some challenges that are unique to Big Data and data analytics.
 
Even though this increase is happening by leaps and bounds, data creation and consumption is on its way up. This encourages greater investments in not just the hardware and software enabling data analytics, but also in data analytics services and the training and education of data scientists.
 
In addition to the above, we witness increased interest and investments in AI and its deep learning subset, encouraged mainly by the availability of massive datasets. The outcome of this is the emergence of new tools for data collection and analysis as well as new roles and responsibilities in the enterprise.
 
The worldwide revenues for Big Data and Business Analytics amounted to $130 billion in 2016. The International Data Corporation (IDC) predicted at the times that this revenue will grow at an annual compoundgrowth rate (CAGR) of 11.7% to reach $203 billion in 2020.
 
While AI and deep learning offer endless possibilities to organizations and data professionals in the collection and analysis of data, they may have to overcome several challenges to get to a point where they can use the data collected to extract useful information that benefits operations and the bottom line.
 
One of the major challenges that organizations and data professionals will have to overcome in this regard is creating a data-driven culture in their organization. According to an annual survey by NewVantage Partners, while more than 80% of the respondents have started programs to create data-driven cultures, less than half of them have been successful in their initiatives so far. Technology is not at fault here instead the problem lies in organizational alignment, general organizational resistance and management understanding.
 
Creating a data-driven culture is not the only challenge that organizations and data professionals need to overcome to get value out of the data collected and its analysis, they also need to overcome other challenges such as:
- Integration
- Analysis and retrieval of real-time insights
- Siloed analytics and compelling results
- Lack of skills to interpret and apply analytics in business context
- Data lakes failure
- Data governance and security
- Data-driven decision making
 
All of these challenges need to overcome for successful data analytics. The widely agreed solution for this is an automated data integration and analytics platform that is easy to implement and use for valuable insights.
 

Top Trends to Look for When Selecting an Automated Data Integration and Analytics Platform

As of 2017, the Advanced Analytics market was valued at approximately $16.58 billion. By 2025, this will grow by almost ten times to reach a valuation of $165.68 billion. That is huge to say the least. According to the “Global Advanced Analytics Market” report, during 2018 and 2022, the Advanced Analytics market will grow at a rate of 33.4%.
 
In addition to the Advanced Analytics market, the global Big Data and Business analytics Market is also predicted to grow significantly during the forecast period 2015 to 2022 to reach $274.3 billion in valuation by year end 2022. Following is an illustration of this:
 
global big data and business analytics market
(The global big data and business analytics market will grow from $122 billion in 2015 to $274.3 billion in 2022 [Image Source])
 
Growth is expected in the Business Analytics market separately as well. According to researchers at MarketsandMarkets, the Business Analytics Market will grow from $17.09 billion in 2016 to $26.88 billion by 2021.
 
Growth is expected in the Business Analytics market
 
(BI Market Size (in USD Billion) by Region in 2021 [Image Source])
 
The growth in both the Advanced Analytics and Business Analytics Market is perhaps best explained by Sachin Janapure, who says:
“The market is growing rapidly because of the transformation from traditional techniques for analyzing business data to advanced business analytics techniques and the massive surge in the volumes structured and unstructured data”
 
MarketsandMarkets also points to four trends that fuel the growth of both the Business Analytics and Advanced Analytics markets. They are:
- Adoption of data-driven decision making
- Adoption of cloud
- Emergence of IoT-enabled technologies
- Growth of Advanced Analytics
 
Today, we have 22 billion connected devices. These devices enable not just the Internet of Things field, but also a new field of data analytics called IoT analytics. The IoT Analytics market is growing at a rapid pace and is expected to reach $27.8 billion by 2022 and $65.6 billion by 2025.
 
iot analytics market is growing
(IoT Analytics Market by Region during forecast period 2015 to 2022 (in USD Billion) [Image Source])
 
A major application area of IoT analytics is the industrial sector where many manufacturing facilities have started to use automated robots for making data-driven decisions and streamlining production. Perhaps, this is the reason it’s predicted that the global cloud data center IP traffic will grow to 19.5 zettabytes by 2021 and 79.4 zettabytes by 2025.
 
global cloud data center ip traffic
(Global cloud data center IP traffic from 2015 to 2021 [Image Source])
 
Today, on-premise reporting systems are being replaced on a large-scale by cloud analytics and business intelligence (BI) platforms, because cloud platforms provide the prototyping tools and easily customized user interfaces that legacy systems lack. According to Statista, in 2022, the Business Intelligence and Analytics Software Application market will grow to $14.5.
 
software application market worldwide
(Size of the business intelligence and analytics software application market worldwide [Image Source])
 
While cloud-based analytics platforms are closing the gap between what legacy systems provide and what enterprises need and want, they are not a surefire solution to your analytics problems.
 
Despite the advancements in technology that cloud analytics platforms offer, businesses continue to struggle with analytics. A major reason for this is that businesses are so mesmerized by the ‘advanced’ analytics technology that they fail to perform a tool evaluation before starting their analytics journey. This is a mistake that can cost most companies dearly.
 
Looking for a cloud-based analytics platform is just one of the several steps in finding the ‘right’ automated data integration and analytics platform for your business. You also need to keep the following top trends in mind when selecting the platform:
 

Data without Boundaries

Business Intelligence (BI) and Analytics platforms, like the other technologies, also need to have AI, NLP, and machine learning capabilities. The BI and analytics platforms you choose must have these three capabilities incorporated into their dashboards and beyond. This is because augmented analytics—enabled by AI, NLP, and machine learning, is said to be thenext disruption in Analytics and AI.
 

Putting Data Analysis in the Hands of the Users

With cloud-based Analytics platforms, generating timely, accurate data analysis is less complicated. However, the proliferation of applications means that data must be stored in several different locations. As such, there is risk of data being inconsistent or inaccurate if it has to be pulled from each application. The good news is that an integration platform as a service (iPaaS) solution incorporated into a multi-purpose PaaS can solve this problem in an extremely cost-effective and scalable manner as it includes both master data management (MDM) and API Management.
 

Embedded Analytics Everywhere

Another trend to look out for when selecting an automated data integration and analytics platform is ‘embedded analytics everywhere’. This means that the data integration and analytics platform you choose should be extensive, have open APIs for extreme customization, and support white labeling.
 

How SAP Aims to Revolutionize Data Management and Analytics

With more and more organizations moving to the cloud, the competition between businesses is becoming incredibly intense. At this point, the only thing that differentiates them is their data. The cloud provides all businesses with the latest software tools and development methodologies. However, businesses that manage and use their data in the cloud better are able to take the lead.
 
This is because a business’ intrinsic value resides in its data. This include all types of data that the business is exposed to such as customer and product data, competitor data, supply chain data, and other data that falls the under the category of ‘big data’.
 
A recent survey reveals that an increasing number of companies are committing to the cloud for data management and analytics. With so many organizations moving to the cloud to manage and use their data better, any business would love a solution that provides them with superior data management and analytics capabilities. The SAP Data Warehouse Cloud provides exactly that.
 

What is SAP Data Warehouse Cloud?

sap data warehouse cloud

(How SAP Data Warehouse Cloud Works Webcast Recap [Image Source])
 
Since all businesses rely on their data, having a properly configured and centralized data warehouse is crucial for any organization. You need to store all of your data in a central place to allow teams across the organization to access it. When teams have easy access to data, they can better run queries and perform complex analysis.
 
This is where on-premise data warehouses are completely outshined by their cloud-based counterparts. Not only are cloud-based date warehouses faster and easier to use than on-premise solutions, but they are also reliable and secure. With cloud data warehouse, you can provide access to data in real time to the entire organization. In short, cloud data warehouse makes it easier and faster to access data and gain valuable insights from it.
 
As data collection is ever increasing, businesses must be able to harness the power of data and transform it to their business value. From that perspective, moving to the cloud helps businesses achieve their goals in their digital transformation journey. Moving to the cloud will mainly help in:
1. Way faster implementation
2. Easier infrastructure
3. Controlled expenses, as companies will pay for what they use. Also, no need for a big chunk of upfront investment. Small monthly payments will suffice
 
However, not all data warehouses are created equal. Some will be less efficient than the others in producing the desired results. In fact, if we were to believe the people at SAP, then all solutions other than the SAP Data Warehouse cloud are nothing more than just databases on the cloud and they can’t be considered a real cloud data warehouse solution.
 
How much weight is in SAP’s argument is something we can only establish by looking into the company’s Data Warehouse solution and finding if there’s anything that sets it apart from the rest.
 
What is the SAP Data Warehouse Cloud? It is a data warehouse cloud solution built with SAP HANA Cloud services. To provide you with a bit of background, SAP HANA Cloud services are services that provide real-time access to data in a distributed landscape. No matter where it stored, users in an organization can have access to all the information they need from a single source of data.
 
Built on the SAP HANA Cloud Services platform, SAP Data Warehouse Cloud provides access to data to users in an organization across all relevant sources. Additionally, it can create insights for the different business needs while keeping the information protected.
 

What Sets the SAP Data Warehouse Cloud Apart from the Competition

There are several things that SAP Data Warehouse Cloud Apart from the competition. Following are some of them:
 
1. The way it’s tailored to the business end user. Not only is data warehouse cloud of SAP useful in building models, but it allows managing users and data, playing with transformations, and scheduling all kinds of data loads. With the SAP Data Warehouse Cloud, businesses will no longer need to delegate tasks to IT as often as they typically do. This is because business domain specific layer is enriched with business terminology to abstract away complexity of the data
 
2. Flexibility and scalability
 
3. Pre-built adapters that will enable end-user to connect to SAP applications right away. Deep integration is inherently part of the system, prebuilt dashboards and reports ready for consumption
 
4. Overall businesses will not have to worry about:
a. Data access
b. Data integrity
c. Long implementation time
d. Huge upfront cost
e. Single point of truth. No need to second guess data
 

How SAP Data Warehouse Cloud is Going to Impact the Current Tech-Stack?

There was a time when organizations would store all of the information in a central place and then lock it up to keep it safe. While this kept the data secure, it prevented people at an organization from extracting any valuable insights from the valuable data. Needless to say, this has proven to be counterproductive.
 
Locking up data in a vault serves no purpose. Instead, data becomes valuable when it is transported from one place to another for sharing and analytics purposes. An organization can bring innovation into its processes only if it has quick and easy access to data in real-time that can be used to extract valuable insights. This is exactly what the SAP Data Warehouse Cloud provides.
 
SAP’s data warehouse solution makes it easier to migrate existing Business Warehouse (BW) systems to cloud. Additionally, this browser-based tool is compatible with all types of devices. With SAP Data Warehouse Cloud, you have enough flexibility to connect across landscapes within the organization. Moreover, SLT & BWA are longer required.
 
The Business Warehouse Accelerator (BWA) is a computer appliance that reads data directly from memory while SLT is a server that transports data from source system to the target system using a trigger-based replication approach.
 
In addition to the above, the SAP Data Warehouse Cloud provides out of the box integration with SAP Analytics cloud. Additionally, it can connect to various SAP and non-SAP sources. These sources include:
- SAP Ariba,
- SAP ASE,
- SAP BW,
- SAP Concur,
- SAP ERP, SAP Fieldglass,
- SAP Hana,
- SAP S/4 Hana,
- SAP Success factors,
- Actian Ingres,
- Adobe Analytics,
- Amazon Aurora,
- Amazon Dynamo,
- Amazon Redshift,
- Apache Spark,
- At Scale,
- Attvo,
- Amazon web services,
- Azure SQL,
- Camel,
- Cassandra,
- Cloudera,
- Couchbase,
- Csv,
- Datastax,
- Denodo,
- Elastic,
- Exasol,
- Excel,
- Gigya,
- Google Ad manager,
- Google Ad words,
- Google Analytics,
- Google Big Query,
- Google Drive,
- Google Sheets,
- Hive,
- Hortonworks,
- IBM DB2,
- IBM MQ,
- Impala,
- Oracle J.D. Edwards,
- Jira,
- Kafka,
- And LinkedIn
 
The above list is not exhaustive, and the SAP Data Warehouse Cloud can connect to many other sources.
 

The Benefits and Challenges of SAP Data Warehouse Cloud for Businesses

 
Following is how the SAP Data Warehouse Cloud can benefit organizations:
- Designed for simplicity goes beyond typical IT to anyone in the business who wants to refer data
- Single point of entry for the analytics
- Ability to share business insights across teams within the organization with a single source of truth data
- Eliminates offline file downloads and analysis
- Real time data analysis from various structured and unstructured data like social media, marketing research data…etc. Endless capabilities
- Natural Language Processing (NLP) capabilities empower simplified questions and automatically generates the intended data insights
- Instantaneous spin off the servers on the cloud in a short amount of time
- Achieve Business Self-Service
- Business users will have their own information space for their analytics purposes
- Easily expandable, reallocate computer resources to meet usage demands
- Space manager has the ability to get live view of the usage across the different spaces. They also have the ability to delete or hibernate the unused spaces. This can help reduce the overall usage of computer resources, thus reducing the usage bills
- Pay by use concept
 
While there are many benefits of using SAP Data Warehouse Cloud for businesses, several challenges need to be overcome before these benefits can be realized. Listed below are some of these challenges:
- Cross-spaces information reporting
- Structural changes behind the table scenarios
- Enhancements to the table scenarios
- Identifying the table relations
- Performance optimization of the information view data models
- Information spaces cleanup
- Change management
- Data security and access provision
 
Once these challenges are overcome, the use of SAP Data Warehouse Cloud will prove useful for businesses.
 

Conclusion

Both the data science and data analytics fields have a bright future ahead, which has been made possible by the increased adoption of the internet and the cloud as well as the developments and advancements in data collection and analysis, business intelligence software, IoT and related analytics, to name a few. The details relating to this have been provided above.

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Author

Harnil Oza

Harnil Oza is a CEO of Hyperlink InfoSystem, a mobile app development company based in USA & India having a team of best app developers who deliver best mobile solutions mainly on Android and iOS platform and also listed as one of the top app development companies by leading research platform. He regularly contributes his knowledge on the leading blogging sites.

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