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The company currently employees 150+ employees, and serves 1000+ clients, clocking 90% customer daily log in, over 20M queries run per day with 30k dashboards being shared per day  

In a Quest to Drive the Future of Businesses

Periscope is on a mission to help data teams transform into superheroes. The company builds software that transforms businesses, by bringing the power of data to every decision. Periscope Data’s platform delivers speed, power and flexibility to the world of BI, by enabling data teams to ingest, store, analyze, visualize and report on data all from one connected platform. This turns data teams into superheroes by empowering them to answer the toughest questions and make breakthrough business decisions. Periscope Data serves more than 1,000 customers globally, including Adobe, Crunchbase, EY, Flexport, New Relic, Rover, and ZipRecruiter. The company’s customers spend more than 20 hours per week within the platform on average, with more than 90 percent of them logging in every day. Those customers create more than 7000 new charts every day, with more and more customers joining every day.  

The Dynamic Leaders Behind Periscope Data

Periscope Data was Co-founded by CEO Harry Glaser and CTO Tom O’Neill in 2012 to address an urgent need from data analysts for lightning-fast investigative analytics. The company is very passionate about the growth of data teams and empowering them with the tools they need to grow their businesses. Enterprises are increasingly bringing data teams together under one roof, and challenging them to provide instant insight to the entire company based on an exponentially increasing amount of data is arduous. Periscope Data built its Unified Data Platform to make data teams more collaborative, and ultimately more productive. Prior to Periscope Data, Glaser was a product manager at Google. In addition to his work at Periscope Data, Glaser is an active member of Project Include, a community for accelerating meaningful, enduring diversity and inclusion in the tech industry. With Glaser’s leadership, Periscope Data has built an incredibly experienced and diverse team that works closely with data teams to address their analytics needs. The company’s customer solutions team was recently recognized among the top customer service teams in the U.S. and Periscope Data has been named a “Best Place to Work” by several local media outlets.  

Innovations are Driven by Emerging Technologies

Periscope Data believes that unsupervised machine learning may be the biggest story of big data in 2023. While not everyone will be using machine learning to make decisions, platforms that focus on flexibility and rapid iteration will outpace those based on rigid structures and processes. Visual-based data discovery tools should enable flexibility to quickly explore new types of questions those that don’t will have serious problems in the future.  

Core Partnerships Taking a Forward Path

Through Periscope’s native connectors and ecosystem of ETL partners, customers can easily connect to all of their data and bring it together in a single platform to power insightful analysis and insights. Amazon Redshift is a core component of Periscope Data’s platform. With customers running millions of queries and creating thousands of charts on its platform each day, Periscope’s partnership with Amazon enables the company to streamline analytics workflows for data teams. Other network of partners includes Snowflake, Alooma BigQuery, Fivetran and many others.  

AI and Machine Learning Offer Growth Promises

As an analytics provider, Periscope Data is witnessing that BI measurement and reporting will be significantly refined with the growth of machine learning and AI. For humans, detecting anomalies in large volumes of data is time-consuming and difficult, especially when large amounts of data flow through a system, AI can point out anomaly detection much easier. There’s huge value in using AI for this detection, then adding humans for the final step. To help data teams have a better control over their data, Periscope Data offers a new Git integration to its customers. Once connected to the Git repository, all user-generated content created in Periscope Data is automatically synced to the related Git repository and saved as a series of files and folders that store reusable code and configurations for Dashboards, Charts, Views, Snippets, Filters and Alerts. This new functionality opens up a lot of new opportunity for the customers to streamline their data workflow while also incorporating new quality checks.  

Recognitions for the Incredible Progress

Periscope Data has been awarded as the winner of 15 Comparably awards for Small & Midsized Companies. Recently, the company’s BI platform has been awarded as the #1 Cloud BI platform by the SaaS Awards delivering unmatched speed, power and flexibility to the world of BI.  

Commitment to the Future

Periscope strives to stay committed to the future. It is excited to continue building tools and features that turn data teams into superheroes transforming their stakeholders use the single, unified platform. In the future, the company aspires to connect maximum data professionals giving them a full control over the analytics lifecycle including ingestion, storage, analysis, visualization and reporting and non-technical users the ability to drill down into the data to quickly answer questions.

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The Benefits Of Using A Customer Data Platform Cdp

A Customer Data Platform looks to fulfil that age-old promise of centralizing all the customer data, and it is looking good

Marketers know that data management is the key to data-driven marketing. Traditional methods for trying to bring customer data together into a “360 customer view”, failed to solve the complete problem. But there is a new player in town, and its called Customer Data Platform (CDP). Now it is knocking on your door. Should you let it in?

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Bringing together customer data in a CDP

An un-siloed and full view of the customer is not a new concept. Yet the 360 customer view has always turned out to be the just out of reach for the marketer. Custom MarTech integration projects are known to be an especially tough cookie.

A Customer Data Platform looks to fulfill that age-old promise of centralizing all the customer data, and it is looking good. In a typical CDP setup there are three layers or functional stages: Data, Decisioning and Delivery. At the minimum CDP works on the Data layer, bringing together and making available the data from multiple sources. Other CDPs also offer extended Decisioning and Delivery functions.

I have written about CDPs, especially the difference between a CDP, CRM and DMPs.

And that is still a thing. More than half of organizations rate their own marketing and customer experience approach to be disconnected. Only 10% finds their MarTech stack is tightly integrated, with Europe doing a bit worse than North America.

But wait… creating a customer profile or even a 360 customer view doesn’t bring any value by itself.

So what are the benefits of using a Customer Data Platform? and would you need one? Let’s look at a case study that explains the value of connected data.

HelloFresh lost customer reactivation case

An example of a Customer Data Platform use case comes from Hellofresh. The HelloFresh food Box subscription service uses many channels. Facebook retargeting via custom audiences, Sendgrid for emails, Appboy for mobile push, Twilio for SMS, their own site(s) and integrated personalized offline letters using Optilyz. They wanted to connect these while keeping the current set-up.

Through the use of CDP they were able to coordinate the different channels, but also do cross-channel campaign testing. They tested several channels against each other and also different combinations (like the winning combination with email + direct mail). Just setting this up would have been an enormous effort without a CDP.

Then bring back the tracked conversions of the different channels and campaigns.

A CDP helps to do so, without the manual effort and while tracking the results for every user.

Overall, the integrated setup allows companies that have a CDP to easily find the right datasets for possible campaigns, then plan the right campaigns for an A/B/C/…-testing and measure the results.

Here is the case presented by CDP CrossEngage. Reactivate or rather win-back former customers and increase the effectiveness of a personalized direct mail.

Sending an email teaser first doubles ROI and Conversion Rate of the direct mail. It makes sense that the results are better than a single Direct mail, but also much better than an email discount and direct mail after. Which makes the case for A/B testing different scenarios.

Still quite a simple campaign if you look at it like this, but automation requires that the all the data and the multiple execution channels here are communicating with each other.

Another finding was that young people react better to the direct mail moment than older. As a result, HelloFresh could optimize their campaign and send direct mails more often to younger people (and spend more budget on this audience).

In my eyes, the less complex you can keep your marketing campaigns, the better. And especially the first iteration of your campaign.

Benefits of unified customer data

Research by the CDP Institute showed that Personalization, Insights and cross-channel measurement rank as the most popular benefits for Unified Customer Data.

You could see why these would be beneficial to marketers. The numbers might be a bit slanted though, as the respondents could answer multiple uses. For instance, if you have a loyalty program (or thinking about one) obviously your would already need to connect all the program data and personalize.

Importance of Time to first Value and Data Activation

If you are trying to create a CDP business case, the Time to First Value is an important consideration. CDP implementation can be faster than custom, single point integrations. This is because of the built-in integrations and data management features. But the value is the use of the data, also called “Data Activation”.

Start with one or two use cases that have a direct impact on profits and can be implemented quickly. This will help to increase internal support for the data-driven marketing as a whole. Then everything after that is gravy.

Later, the use-case can be built out, or more uses added. A first use case is often a lighthouse project. By having the infrastructure in place, the first project also has a signal effect for numerous follow-up projects.

Think about the maturity on dimensions a CDP offers:

Level of personalization: from placeholders for names to individual website and email content as well as individual customer journeys

Level of automation and refined Decisioning

Level of segmentation: from big groups to almost 1:1 communication

Data maturity: add more data points, from profile, product, and behavioral data to intent and value based.

Add more touchpoints and channels for Cross-channel campaigns.

Begin with quick-wins so the CDP can pay for itself. The quick-wins come in different forms. Sometimes it is called a pilot, a Minimum Viable Product (MVP) or the use of the CDP in a single campaign. While the roadmap will be different – the stages look the same. Starting small with an implementation time of one to three months to reach first results.

Finding your use cases and the Benefits of a CDP

The benefits of a Customer Data Platform boil down your own use case(s). These will provide the value of the CDP. Yet there are some other benefits that go beyond the single use cases and more into the long-term vision of company data management.

1. Cross-channel attribution

While the current measurement and attribution maturity of your company might not demand it (yet). The Case of HelloFresh shows that running campaigns over multiple channels asks for a flexible reporting and attribution. The CDP will ready the organization for a more comprehensive view of the outcomes of Marketing activities and attribution across channels.

2. Agility and future-ready infrastructure

A CDP is built to be a central hub to connect data sources and delivery platforms, where sources can be connected as they are introduced and use it anywhere to drive better customer experiences.

3. Democratization of Data

Traditional IT-managed databases have a built-in bottleneck (namely the IT resources). A CDP democratizes the access to customer data and lets the data be used directly by the departments that generate the associated value. Marketing, customer service, business intelligence, they all depend on the access to the data and customer touchpoints.

Hql Commands For Data Analytics

HQL or Hive Query Language is a simple yet powerful SQL like querying language which provides the users with the ability to perform data analytics on big datasets. Owing to its syntax similarity to SQL, HQL has been widely adopted among data engineers and can be learned quickly by people new to the world of big data and Hive.

In this article, we will be performing several HQL commands on a “big data” dataset containing information on customers, their purchases, InvoiceID, their country of origin and many more. These parameters will help us better understand our customers and make more efficient and sound business decisions.

For the purpose of execution, we would be using Beeline command Line Interface which executes queries through HiveServer2.

Next, we type in the following command which connects us to the HiveServer2.

It requires authentication so we input the username and password for this session and provide the location or path where we have our database stored. The commands(underlined in red) for this are given below.

set chúng tôi = /user/username/warehouse;

Now that we are connected to HiveServer2 we are ready to start querying our database. Firstly we create our database “demo01” and then type in the command to use it.

Use demo01;

Now we are going to list all the tables present in the demo01 database using the following command

show tables;

As we can see above 2 tables “emp” and “t1” are already present in the demo01 database. So for our customer’s dataset, we are going to create a new table called “customers”.

CREATE TABLE IF NOT EXISTS customers (InvoiceNo VARCHAR(255),Stock_Code VARCHAR(255),Description VARCHAR(255),Quantity INT,UnitPrice DECIMAL(6,2),CustomerID INT,Country VARCHAR(255)) ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' ;

Now if we run the “show tables” command we see the following output.

We can see that a table named customers has been created in the demo01 database. We can also see the schema of the table using the following command.

desc customers;

Now we upload our chúng tôi file containing customer records to our hdfs storage using this command.

hdfs dfs -put chúng tôi /user/itv001775/warehouse/demo01.db/customers

Now we have to load this data into the customer’s table we created above. To do this we run the following command.

load data inpath '/user/itv001775/warehouse/demo01.db/customers/new.csv' into table customers;

This concludes the part where we have uploaded the data on hdfs and loaded it into the customer’s table we created in the demo01 database.

Now we shall do a bit of data eyeballing meaning to have a look at the data and see what insights can be extracted from it. As the dataset contains over 580,000 records we shall have a look at the first 5 records for convenience using this command.

select * from customers limit 5;

We can see above it has 7 columns namely invoiceno, stock_code, description, quantity, unitprice,  customerid and country. Each column brings value and insights for the data analysis we are going to perform next.

DATA ANALYSIS THROUGH HQL

 

stock code 3427AB, quantity 2 at a unit price of 9000.

QUERY:

insert into customers values (‘610221’,’3427AB’,’Gaming PC’,2,9000,18443,’Germany’);

Now we can query the database to see if the record was inserted successfully.

select * from customers limit 5;

As we can see record has been inserted.

CASE 2: We want to see the sum of the purchases made by each customer along with invoiceno in descending order.

QUERY: (for convenience we limit our output to the first 10 records)

select customerid, sum(unitprice) as total_purchase from customers group by customerid order by total_purchase desc limit 10;

In the above query, we are grouping our records together based on the same customers id’s and ordering the results by total purchase made by each customer.

Apart from the customers without a customerid, we are able to find out our top 10 customers according to the amount of their purchase. This can be really helpful in scouting and targeting potential customers who would be profitable for businesses.

CASE 3: We want to find out the average price of bags being sold to our customers.

QUERY:

select avg(unitprice) as average_bagPrice from customers where description like '%BAG%';

Note that in the above query we used the “like” logical operator to find the text from the description field. The “%” sign represents that anything can come before and after the word “bag” in the text.

We can observe that the average price across the spectrum of products sold under the tag of bags is 2.35(dollars, euros or any other currency). The same can be done for other articles which can help companies to determine the price ranges for their products for better sales output.

price of products for top 10 countries in descending order by count.

QUERY: 

select count(*) as number_of_products, sum(unitprice) as totalsum_of_price, country from customers group by country order by totalsum_of_price desc limit 10;

Here count(*) means counting all the records separately for each country and ordering the output by total sum of price of goods sold in that country.

Through this query, we can infer the countries the businesses should target the most as the total revenue generated from these countries is maximum.

quantity for top 20 customers.

QUERY:

For each customer, we are grouping their records by their id and finding the number of products they bought in descending order of that statistic. We also employ the condition that only those records are selected where a number of products are greater than 10.

Note that we always use the “having” clause with the group by when we want to specify a condition.

Through this, we can see our top customers based on the number of products they ordered. The customers ordering the most generate the most amount of profit for the company and thus should be scouted and pursued the most, and this analysis helps us find them efficiently.

BONUS

Hive has an amazing feature of sorting our data through the “Sort by” clause. It almost does the same function as the “order by” clause as in they both arrange the data in ascending or descending order. But the main difference can be seen in the working of both these commands.

We know that in Hive, the queries we write in HQL are converted into MapReduce jobs so as to abstract the complexity and make it comfortable for the user.

So when we run a query like :

Select customerid, unitprice from customers sort by customerid;

Multiple mappers and reducers are deployed for the MapReduce job. Give attention to the fact that multiple reducers are used which is the key difference here.

Multiple mappers output their data into their respective reducers where it is sorted according to the column provided in the query, customerid in this case. The final output contains the appended data from all the reducers, resulting in partial sorting of data.

Whereas in order by multiple mappers are used along with only 1 reducer. Usage of a single reducer result in complete sorting of the data passed on from the mappers.

Select customerid, unitprice from customers order by customerid;

The difference in the Reducer output can be clearly seen in the data. Hence we can say that “order by” guarantees complete order in the data whereas “sort by” delivers partially ordered results.

ENDNOTES

In this article, we have learned to run HQL queries and draw insights from our customer dataset for data analytics. These insights are valuable business intelligence and are very useful in driving business decisions.

Read more articles on Data Analytics here.

Related

Big Data Definition & Analytics

The sheer volume of big data continues to grow as corporations and consumers depend on mobile devices, streaming, the Internet of Things (IoT), and other technologies that collect and use massive quantities of data. 

And with the growth of big data, the market is seeing a steady increase of tools and capabilities for storing and analyzing it. 

See below to learn about the top tools in the big data market and how enterprises are benefiting from the increased insights borne from big data.

“Big data” was first coined in the 1990s when technology experts began to recognize the quickly expanding pools of data in enterprises as well as the growing problem of processing and applying that data with existing technology.

Big data has grown across industries throughout the 21st century, and with that growth has come the development of different big data tools to handle the data.

Big data informs several business decisions and operations, but is especially helpful in the following categories:

Customer Analytics

Operational Analytics

Fraud Detection

Compliance

Data Warehouse Optimization

Read next: The Pros and Cons of Edge Computing

The global big data market reached $208 billion in 2023 and is projected for a steady compound annual growth rate of 10%, reaching $450 billion by 2026, according to Expert Market Research. 

The growth is mostly attributed to a growing desire to make all business data actionable in a competitive marketplace, with the growth of IoT devices contributing to the expansion of big data solutions.

Although North America is leading the market in big data software adoption and general strategy, China is the fastest-growing country in this predicted time period, with Australia, India, and South Korea also increasing their big data investments at a healthy rate.

In order for software to be classified as appropriate for big data management, it must meet the criteria of the “3 Vs”: variety, volume, and velocity:

Variety:

The tool is able to work with a variety of data types, whether structured, unstructured, quantitative, or qualitative.

Volume:

Big data consists of terabytes and petabytes of data to manage. Big data software must have the capacity to store/analyze higher volumes of data than traditional data tools as a result.

Velocity:

Especially in a fast-developing IoT marketplace, big data solutions have to be able to work with data quickly and in real-time in order to produce desired results.

More on data variety: Structured vs. Unstructured Data

More on data variety: Qualitative vs. Quantitative Data

Understanding big data provides huge benefits to organizations that take the time to set up, uncover and analyze their data. 

Some of the top benefits that companies realize when they tap into their big data potential include:

A better understanding of customer behavior at various stages based on large amounts of data that might have previously gone unanalyzed. This information is particularly helpful in the entertainment, e-commerce, and tourism industries.

For organizations that want to simplify their network security and operations practices, big data makes it possible to develop AIOps and automate different network functions, such as application performance management and network monitoring. This frees up time for network administrators to spend on more strategic projects.

In industries, such as finance, banking, government, healthcare, and others with frequent and large transactions, big data analytics improves fraud detection, risk management, and general compliance.

Customers across industries use big data tools to make sense of their customer and product data at scale. 

They frequently rely on the big data analytics in these tools, but perhaps more importantly, they use these tools for the data visualizations and reports that make big data digestible for non-data professionals:

“We have been using [Hitachi Vantara’s] Pentaho Business Analytics for the past 3 years in our department due to its best services for data visualization and data analytics. It is open and easily added to any platform. It provides such an easy UI that non-technical persons can get the use and analysis results. Easily configurable and deployed at our organization.” –Software manager, manufacturing industry, software review at Gartner Peer Insights

Some of the top big data solutions in the market are:

Amazon Web Services:

AWS’s solutions for big data include cloud storage, databases, data warehousing, analytics, and machine learning services.

Hitachi Vantara:

This lineup features big data storage, DataOps, IoT services, and big data analytics.

Tableau:

The Salesforce-acquired tool offers big data analytics, business intelligence, and data visualizations

Cloudera:

This big data platform offers a Hadoop distribution, plus data science and analytics tools.

Microsoft Azure

: The cloud platform offers storage, big data analytics, machine learning, data warehousing, and data lakes.

IBM:

IBM’s big data solutions include cloud services, database management, data warehousing, analytics, and machine learning.

Oracle:

The Oracle suite of big data solutions includes cloud-based and on-premises database management, data integration, and analytics.

Splunk:

This offering primarily focuses on analytics for log and security data.

Talend:

The solution features a set of big data integration tools.

RapidMiner:

The data science platform includes data mining, predictive analytics, and machine learning solutions

Finding the right DBMS solution: Oracle Database vs. Microsoft SQL Server

Learn more about what’s happening in big data here.

How Big Data Analytics Is Transforming The Casino Industry

As one of the world’s most cutthroat markets, it is no surprise that the casino business has jumped on the big data analytics bandwagon. Casinos may improve their tracking of consumer behaviour, learn more about their patrons’ requirements and preferences, and streamline their operations using big data analytics. Notably, some of the casinos reviewed on SF Gate are utilizing big data analytics to tailor their offering to suit specific customers. The article takes an in-depth look into how casinos utilize big data analytics to streamline their operations. 

Identifying customer preferences and trends

With big data analytics, casinos may better understand their customers’ tastes and behavior by gathering, analyzing, and drawing conclusions from massive volumes of data. Customer profiles, purchase histories, gaming tendencies, loyalty program participation, and other types of information may be collected. Casinos may use this data to understand their patrons better and cater to their needs. For instance, they may be able to tell which games are the most well-liked among people of a certain age or gender. In addition, they could ascertain which campaigns are most successful in generating client engagement or loyalty. Now that they have this information, they may make educated guesses about improving their business for optimum profit.

Promoting responsible gambling practices

There has been a rise in the importance of big data as a tool for responsible gaming. Operators may learn a lot about their customers’ habits by gathering and analyzing a lot of data. The data may help identify problem gamblers before they fall too far into debt. One other way operators may benefit from big data is by gaining a deeper understanding of client preferences and using that knowledge to improve the quality of their offerings. The operators may provide more customized incentives and promotions for each player by, for instance, monitoring their spending habits.

Ensuring customer privacy using big data analytics

There are several measures casinos use to protect their client’s personal information. Encryption is one of the most vital methods. Casinos utilize sophisticated encryption methods to protect all player information, making it almost difficult for unauthorized parties to decrypt player records. To further ensure the privacy of their patrons, casinos often use anonymization methods. Before using data for analytics, it must be cleansed of any identifying characteristics. To round things out, casinos have stringent procedures limiting who may access consumer data and how it can be utilized. To guarantee that the most up-to-date measures always protect the privacy of their customers, casinos examine and revise their policies regularly.

Automated decision making using big data analytics

Implementing fully automated decision-making might drastically alter the future of online gambling. Casinos can now make better, more efficient judgments regarding gameplay thanks to the use of algorithms and AI. The casino’s bottom line and the patrons’ overall satisfaction both stand to benefit from this. By identifying questionable behavior and taking action, automated decision-making might also assist in eliminating fraud and cheating in online casinos. Further, player data might be analyzed using automated decision-making to learn about preferences and design games appropriately. Lastly, automated decision-making might be used in developing new games and enhancing current ones to maintain player interest and satisfaction. As a result of all these positive implications, it’s evident that automated decision-making merits additional exploration for its possible uses in the online gambling sector.

Challenges arising from implementing big data analytics

The massive volume of data is a significant obstacle for casinos attempting to use big data analytics. Customers’ gaming habits, financial transactions, and preferences contribute to the massive amounts of data generated by casinos. Gaining valuable insights into client behavior requires collecting, analyzing, and storing this information which is challenging. Casinos also must safeguard this information against hacking attempts. Validating the accuracy and consistency of the analytics is another obstacle to overcome. Last but not least, casinos should think about how they will put the results of big data analytics to use to boost their business. 

Predictive analysis using big data analytics

Lastly, casinos may use predictive analytics to jump on the competition and maintain long-term profitability by predicting industry trends. Using predictive analytics, casinos can see how varied incentives affect patrons’ propensity to return and spend. It enables businesses to optimize their offerings for maximum profit with little sacrifice to client satisfaction.

5 Top Trends In The Data Analytics Job Market

Data analytics jobs have been well paid and in high demand for some time.

The “IT Skills and Certifications Pay Index” by Foote Partners shows that such skills often merit a pay premium, and the average salary of these specialists has been steadily rising. Among the high-paying areas currently are risk analytics, big data analytics, data science, prescriptive analytics, predictive analytics, modeling, Apache Hadoop, and business analytics. 

But data analytics is a broad term. It encompasses business intelligence (BI) and visualization as well as the application of analytics to other functions, such as IT and cybersecurity. 

Here are some of the five top trends in data analytics jobs: 

See more: The Data Analytics Job Market

Experience or certification in a specific programming language or analytics discipline used to be a passport to good jobs. It will still gain people some positions, but they need more if they hope to move up the pay scale. 

“For analytics professionals, listing proficiency in SAS, Python, or R may get someone past the initial HR screening, but that’s about it,” said Sean O’Brien, SVP of education at SAS. 

Data analytics candidates need experience, certification, and other human skills to succeed in today’s market. 

It used to be enough to crunch some numbers and then tell the business an outcome or prediction using regular language.

These days, executives demand more. A top trend for data analytics jobs is the increasing importance of communication skills and storytelling. The rise of chief data officers and chief analytics officers is the clearest indication that analytics has moved from the backroom to the boardroom, and more often, it’s data experts that are setting strategy.

“The ability to make analytics outputs relatable to stakeholders across the business will set them apart,” said O’Brien with SAS.

“It’s not enough to be able to clean, integrate, and analyze huge amounts of data. Analytics pros have to understand how data and analytics directly support business goals and be able to communicate the story the data is telling. They need to be able to not just present trends and reports but communicate their meaning.” 

Cybersecurity trends apply to data analytics in two ways: Analysts need to be aware of and possess some security skills if they are to keep their platforms and models secure. But perhaps even more importantly, analytics jobs are becoming available in greater frequency in security. Analysts are needed who can unlock the vast troves of data available in system logs, alerts, and organizational data to find the potential incursions and isolate threats. 

“Flexibly and securely viewing trusted data in context through shared applications across an industry ecosystem also enables process and governance improvement,” said Jeffrey Hojlo, an analyst at IDC.

Storage, too, has transitioned into the analytics arena. Storage administrators are spending less time managing storage devices and more time managing data. This entails being more strategic about data mobility, data management, data services, and delivering the foundation for generating value from unstructured data. 

“Storage administrators must leverage analytics about files, such as types of files, access times, owners, and other attributes,” said Randy Hopkins, VP of global systems engineering and enablement at Komprise.

See more: Top Data Analytics Certifications

Risk is a hot area across the business world. And it is up to risk management and risk analysts to identify, analyze, and accept or mitigate any uncertainty that may exist in business or investment decisions.

A variety of tactics are used to determine risk. For example, a common tool is known as standard deviation, which is a statistical measure where data is plotted around a central tendency. Management can then see how much risk might be involved and how to minimize that risk. 

Those skilled in modern risk analytics are now in greater demand, as the risk management field transitions from manual or traditional methods. Accordingly, risk analytics and risk assessment jobs rose by 5.3% in value over a six-month period, according to surveys by Foote Partners. This form of business intelligence exploits structured and unstructured data as a way to model scenarios and outcomes and provide insight into potential fraud, market risk, credit risk, financial risk, supply chain risk, and other areas of risk. 

As a sign that there was definite substance to the hype around big data, Foote Partners notes that big data analytics jobs continue to be in demand. They have risen in value by 13.3% over a six-month period. 

See more: 10 Top Companies Hiring for Data Analytics Jobs

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