Big Data Warehousing with Elasticsearch

| October 12, 2016

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Data processing is more than just storage, consolidation, or aggregation, and even the "4 Vs"do not always cover all of the challenges associated with modern Big Data solutions. Read on to find out how to address the technical challenges and achieve Big Data project’s defined goals using Elasticsearch.

Spotlight

Sannsyn AS

Exploiting vast new flows of information can radically improve your company's performance. There is a lot of signals in the noise. Is your company utilizing your data and Big Data in real time to improve decision making and sales? Sannsyn is a predictive analytics company that develops technology and offers consulting service in this domain. Our core offering is a our Digital Personalization Engine delivered as a Saas platform. The company consist of analysts, data scientists and developers who currently help the likes of Ark, Bokkilden, University of Oslo and others to harness the potential in Big Data. Contact us to discuss your possibilities within Big Data.

OTHER ARTICLES

Data Analytics: Five use cases in telecom industry

Article | May 27, 2021

The telecom industry has witnessed spectacular growth since its establishment in the 1830s. Enabling distant communications, collaborations, and transactions globally, telecommunication plays a significant role in making our lives more convenient and easier. With enhanced flexibility and advanced communication methods, the telecom industry gains more customers and creates new revenue streams. According to Grand View Research, the global telecom market size would expand at a compound annual growth rate (CAGR) of 5.4% between 2021-2028. With the rapidly growing digital connectivity, the communication service providers (CSPs) have to deal with large datasets. Datasets that can allow them better to understand their customers, competitors, industry trends and derive valuable insights for decision making.

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Data Analytics the Force Behind the IoT Evolution

Article | May 27, 2021

Primarily,the IoT stack is going beyond merely ingesting data to data analytics and management, with a focus on real-time analysis and autonomous AI capacities. Enterprises are finding more advanced ways to apply IoT for better and more profitable outcomes. IoT platforms have evolved to use standard open-source protocols and components. Now enterprises are primarily focusing on resolving business problems such as predictive maintenance or usage of smart devices to streamline business operations.Platforms focus on similar things, but early attempts at the creation of highly discrete solutions around specific use cases in place of broad platforms, have been successful. That means more vendors offer more choices for customers, to broaden the chances for success. Clearly, IoT platforms actually sit at the heart of value creation in the IoT.

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BIG DATA MANAGEMENT

How Should Data Science Teams Deal with Operational Tasks?

Article | May 27, 2021

Introduction There are many articles explaining advanced methods on AI, Machine Learning or Reinforcement Learning. Yet, when it comes to real life, data scientists often have to deal with smaller, operational tasks, that are not necessarily at the edge of science, such as building simple SQL queries to generate lists of email addresses to target for CRM campaigns. In theory, these tasks should be assigned to someone more suited, such as Business Analysts or Data Analysts, but it is not always the case that the company has people dedicated specifically to those tasks, especially if it’s a smaller structure. In some cases, these activities might consume so much of our time that we don’t have much left for the stuff that matters, and might end up doing a less than optimal work in both. That said, how should we deal with those tasks? In one hand, not only we usually don’t like doing operational tasks, but they are also a bad use of an expensive professional. On the other hand, someone has to do them, and not everyone has the necessary SQL knowledge for it. Let’s see some ways in which you can deal with them in order to optimize your team’s time. Reduce The first and most obvious way of doing less operational tasks is by simply refusing to do them. I know it sounds harsh, and it might be impractical depending on your company and its hierarchy, but it’s worth trying it in some cases. By “refusing”, I mean questioning if that task is really necessary, and trying to find best ways of doing it. Let’s say that every month you have to prepare 3 different reports, for different areas, that contain similar information. You have managed to automate the SQL queries, but you still have to double check the results and eventually add/remove some information upon the user’s request or change something in the charts layout. In this example, you could see if all of the 3 different reports are necessary, or if you could adapt them so they become one report that you send to the 3 different users. Anyways, think of ways through which you can reduce the necessary time for those tasks or, ideally, stop performing them at all. Empower Sometimes it can pay to take the time to empower your users to perform some of those tasks themselves. If there is a specific team that demands most of the operational tasks, try encouraging them to use no-code tools, putting it in a way that they fell they will be more autonomous. You can either use already existing solutions or develop them in-house (this could be a great learning opportunity to develop your data scientists’ app-building skills). Automate If you notice it’s a task that you can’t get rid of and can’t delegate, then try to automate it as much as possible. For reports, try to migrate them to a data visualization tool such as Tableau or Google Data Studio and synchronize them with your database. If it’s related to ad hoc requests, try to make your SQL queries as flexible as possible, with variable dates and names, so that you don’t have to re-write them every time. Organize Especially when you are a manager, you have to prioritize, so you and your team don’t get drowned in the endless operational tasks. In order to do this, set aside one or two days in your week which you will assign to that kind of work, and don’t look at it in the remaining 3–4 days. To achieve this, you will have to adapt your workload by following the previous steps and also manage expectations by taking this smaller amount of work hours when setting deadlines. This also means explaining the paradigm shift to your internal clients, so they can adapt to these new deadlines. This step might require some internal politics, negotiating with your superiors and with other departments. Conclusion Once you have mapped all your operational activities, you start by eliminating as much as possible from your pipeline, first by getting rid of unnecessary activities for good, then by delegating them to the teams that request them. Then, whatever is left for you to do, you automate and organize, to make sure you are making time for the relevant work your team has to do. This way you make sure expensive employees’ time is being well spent, maximizing company’s profit.

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Top 6 Marketing Analytics Trends in 2021

Article | May 27, 2021

The marketing industry keeps changing every year. Businesses and enterprises have the task of keeping up with the changes in marketing trends as they evolve. As consumer demands and behavior changed, brands had to move from traditional marketing channels like print and electronic to digital channels like social media, Google Ads, YouTube, and more. Businesses have begun to consider marketing analytics a crucial component of marketing as they are the primary reason for success. In uncertain times, marketing analytics tools calculate and evaluate the market status and enhances better planning for enterprises. As Covid-19 hit the world, organizations that used traditional marketing analytics tools and relied on historical data realized that many of these models became irrelevant. The pandemic rendered a lot of data useless. With machine learning (ML) and artificial intelligence (AI) in marketers’ arsenal, marketing analytics is turning virtual with a shift in the marketing landscape in 2021. They are also pivoting from relying on just AI technologies but rather combining big data with it. AI and machine learning help advertisers and marketers to improve their target audience and re-strategize their campaigns through advanced marketing attributes, which in turn increases customer retention and customer loyalty. While technology is making targeting and measuring possible, marketers have had to reassure their commitment to consumer privacy and data regulations and governance in their initiatives. They are also relying on third-party data. These data and analytics trends will help organizations deal with radical changes and uncertainties, with opportunities they bring with them over the next few years. To know why businesses are gravitating towards these trends in marketing analytics, let us look at why it is so important. Importance of Marketing Analytics As businesses extended into new marketing categories, new technologies were implemented to support them. This new technology was usually deployed in isolation, which resulted in assorted and disconnected data sets. Usually, marketers based their decisions on data from individual channels like website metrics, not considering other marketers channels. Website and social media metrics alone are not enough. In contrast, marketing analytics tools look at all marketing done across channels over a period of time that is vital for sound decision-making and effective program execution. Marketing analytics helps understand how well a campaign is working to achieve business goals or key performance indicators. Marketing analytics allows you to answer questions like: • How are your marketing initiatives/ campaigns working? What can be done to improve them? • How do your marketing campaigns compare with others? What are they spending their time and money on? What marketing analytics software are they using that helps them? • What should be your next step? How should you allocate the marketing budget according to your current spending? Now that the advantages of marketing analytics are clear, let us get into the details of the trends in marketing analytics of 2021: Rise of real-time marketing data analytics Reciprocation to any action is the biggest trend right now in digital marketing, especially post Covid. Brands and businesses strive to respond to customer queries and provide them with solutions. Running queries in a low-latency customer data platform have allowed marketers to filter the view by the audience and identify underachieving sectors. Once this data is collected, businesses and brands can then readjust their customer targeting and messaging to optimize their performance. To achieve this on a larger scale, organizations need to invest in marketing analytics software and platforms to balance data loads with processing for business intelligence and analytics. The platform needs to allow different types of jobs to run parallel by adding resources to groups as required. This gives data scientists more flexibility and access to response data at any given time. Real-time analytics will also aid marketers in identifying underlying threats and problems in their strategies. Marketers will have to conduct a SWOT analysis and continuously optimize their campaigns to suit them better. . Data security, regulatory compliance, and protecting consumer privacy Protecting market data from a rise in cybercrimes and breaches are crucial problems to be addressed in 2021. This year has seen a surge in data breaches that have damaged businesses and their infrastructures to different levels. As a result, marketers have increased their investments in encryption, access control, network monitoring, and other security measures. To help comply with the General Data Protection Regulation (GDPR) of the European Union, the California Consumer Privacy Act (CCPA), and other regulatory bodies, organizations have made the shift to platforms where all consumer data is in one place. Advanced encryptions and stateless computing have made it possible to securely store and share governed data that can be kept in a single location. Interacting with a single copy of the same data will help compliance officers tasked with identifying and deleting every piece of information related to a particular customer much easier and the possibility of overseeing something gets canceled. Protecting consumer privacy is imperative for marketers. They offer consumers the control to opt out, eradicate their data once they have left the platform, and remove information like location, access control to personally identifiable information like email addresses and billing details separated from other marketing data. Predictive analytics Predictive analytics’ analyzes collected data and predicts future outcomes through ML and AI. It maps out a lookalike audience and identifies which strata are most likely to become a high-value customer and which customer strata has the highest likelihood of churn. It also gauges people’s interests based on their browsing history. With better ML models, predictions have become better overtime, leading to increased customer retention and a drop in churn. According to the research by Zion Market Research, by 2022, the global market for predictive analytics is set to hit $11 billion. Investment in first-party data Cookies-enabled website tracking led marketers to know who was visiting their website and re-calibrate their ads to these people throughout the web. However, in 2020, Google announced cookies would be phased out of Chrome within two years while they had already removed them from Safari and Firefox. Now that adding low-friction tracking to web pages will be tough, marketers will have to gather more limited data. This will then be then integrated with first-party data sets to get a rounded view of the customer. Although a big win for consumer privacy activists, it is difficult for advertisers and agencies to find it more difficult to retarget ads and build audiences in their data management platforms. In a digital world without cookies, marketers now understand how customer data is collected, introspect on their marketing models, and evaluate their marketing strategy. Emergence of contextual customer experience These trends in marketing analytics have become more contextually conscious since the denunciation of cookies. Since marketers are losing their data sets and behavioral data, they have an added motivation to invest in insights. This means that marketers have to target messaging based on known and inferred customer characteristics like their age, location, income, brand affinity, and where these customers are in their buying journey. For example, marketers should tailor messaging in ads to make up consumers based on the frequency of their visits to the store. Effective contextual targeting hinges upon marketers using a single platform for their data and creates a holistic customer profile. Reliance on third-party data Even though there has been a drop in third-party data collection, marketers will continue to invest in third-party data which have a complete understanding of their customers that augments the first-party data they have. Historically, third-party data has been difficult to source and maintain for marketers. There are new platforms that counter improvement of data like long time to value, cost of maintaining third-party data pipelines, and data governance problems. U.S. marketers have spent upwards of $11.9 billion on third-party audience data in 2019, up 6.1% from 2018, and this reported growth curve is going to be even steeper in 2021, according to a study by Interactive Advertising Bureau and Winterberry Group. Conclusion Marketing analytics enables more successful marketing as it shows off direct results of the marketing efforts and investments. These new marketing data analytics trends have made their definite mark and are set to make this year interesting with data and AI-based applications mixed with the changing landscape of marketing channels. Digital marketing will be in demand more than ever as people are purchasing more online. Frequently Asked Questions Why is marketing analytics so important? Marketing analytics has two main purposes; to gauge how well your marketing efforts perform and measure the effectiveness of marketing activity. What is the use of marketing analytics? Marketing analytics help us understand how everything plays off of each other and decide how to invest, whether to re-prioritize or keep going with the current methods. Which industries use marketing analytics? Commercial organizations use it to analyze data from different sources, use analytics to determine the success of a marketing campaign, and target customers specifically. What are the types of marketing analytics tools? Some marketing analytics’ tools are Google Analytics, HubSpot Marketing Hub, Semrush, Looker, Optimizely, etc. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "Why is marketing analytics so important?", "acceptedAnswer": { "@type": "Answer", "text": "Marketing analytics has two main purposes; to gauge how well your marketing efforts perform and measure the effectiveness of marketing activity." } },{ "@type": "Question", "name": "What is the use of marketing analytics?", "acceptedAnswer": { "@type": "Answer", "text": "Marketing analytics help us understand how everything plays off of each other and decide how to invest, whether to re-prioritize or keep going with the current methods." } },{ "@type": "Question", "name": "Which industries use marketing analytics?", "acceptedAnswer": { "@type": "Answer", "text": "Commercial organizations use it to analyze data from different sources, use analytics to determine the success of a marketing campaign, and target customers specifically." } },{ "@type": "Question", "name": "What are the types of marketing analytics tools?", "acceptedAnswer": { "@type": "Answer", "text": "Some marketing analytics’ tools are Google Analytics, HubSpot Marketing Hub, Semrush, Looker, Optimizely, etc." } }] }

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Spotlight

Sannsyn AS

Exploiting vast new flows of information can radically improve your company's performance. There is a lot of signals in the noise. Is your company utilizing your data and Big Data in real time to improve decision making and sales? Sannsyn is a predictive analytics company that develops technology and offers consulting service in this domain. Our core offering is a our Digital Personalization Engine delivered as a Saas platform. The company consist of analysts, data scientists and developers who currently help the likes of Ark, Bokkilden, University of Oslo and others to harness the potential in Big Data. Contact us to discuss your possibilities within Big Data.

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