How to Structure Your Site Architecture like a Data Scientist Guru
Every business needs an easy way to truly understand their web data and mobile site visitors, but does that mean you have to hire a data scientist to ask the right questions from your data?
Data Science architecture is bringing ideas together that transforms data into actionable knowledge. The excitement in this is the ability to make more decisions that solve business revenue problems. To engage semantic SEO, it is necessary to have a strategy, tactics, and the right tools for a structured approach to data. The modern web demands connected content is based on the context of the user, the recognition of related data entities, and how they are connected to the searches made. An understanding of Natural Language, users, KPI’s, and your structured data architecture is fundamental to generating better search results.
By gaining a deeper knowledge about a web site’s core UX architecture, you can use it to store and analyze data, specifically structured data. Create a clear business strategy for how to use Google analytics data to compete and deploy the right UX, technology, and information architecture to drive user engagement. Adjusting your business culture to create digital content that drives mobile search results and conversions requires a multifaceted approach.
Ask any successful digital marketer as to what they regard as core prerequisites for a successful SEO marketing campaign or your paid search investment, and undoubtedly most responses will say something about obtaining or making wise integrations from data points. It helps then, to love data and be savvy enough to leverage it correctly. If a client or someone on the team provides an exhaustive list of metrics that they want to see data reports on, delve into the reasoning behind it to ensure that they are the right fit for their business goals, and ultimately then manage your AdWords metrics and advertising accordingly.
Let’s lay a foundational understanding of terms used in this article to start.
What is Data Science Architecture?
Data Science Architecture is the practice of data science when choosing the best site architecture for global data distribution. It encompasses a data lake architecture to ingest varying dimensions of data from different sources such as the Internet of Things (IoT), clickstream activity on websites, online transaction processing (OLTP) data, both off-site and on-site data, to name just a few. One end goal is to win more clicks in immediate Google SERPs.
What is Data Science?
Data science is the interdisciplinary practice that is concerned about processes and systems to extract knowledge or insights from data in various formats, both structured or unstructured, useful to provide a continuation for data analysis fields such as statistics, machine learning, data mining, and predictive analytics. It is similar to Knowledge Discovery in Databases (KDD).
What is a Data Science Architect?
The data science architect (DSA) determines the design of the data collection, storage and data analysis processes, and demands on the business for time and cost trade-offs involved. It ensures easy crawling and indexing of a site’s architecture.
What is a Graph Database?
In data computing, a graph database (GDB) is a database that utilizes graph structures for semantic search queries with nodes, edges, and properties to represent entities and store data. The system’s core is the graph, or edge or relationship entity mapping. The graph correlates the data items in the store to a collection of nodes and edges, the edges tie together the relationships between the nodes.
What is path in graph data structure?
A directed path, also know as dipath, in a directed graph is a finite or infinite serial sequence of edges that join a progression of distinct vertices, but with the added requirement that the edges be all directed in the same direction.
Machine Learning Data Pipelines for a Data Science Architecture
A data science architecture lays the foundation for better user experiences and product datasets.
One trend that has a whole crowd of passionate semantic gurus is the development of libraries that make machine learning more accessible than ever. Libraries that further automate the building of machine learning data pipelines are TPOT and AutoML/auto-sklearn. Data plus algorithms are the backbone of machine learning that producers intelligent outcomes.
When grappling with initial concepts of machine learning, one might be full of starry-eyed wonder as if it is all magic. In reality, it is a lot more than just “in goes the data; outcome predictive gems. There’s a lot of logic applied to it. Filtered data, algorithms, deep learning and models created by processing the data through the algorithms.
If you’re in the business of deriving actionable insights from data through machine learning, it helps for the process not lost in wonder about. The more you understand it, the better you’ll be able to transform data into useful predictions, and the more powerful they can be to increase revenue streams. Machine learning models store up applicable stored data, which leaves businesses needing to find a way to handle these large data files.
However, interpreting the outcomes of predictive modeling tasks and evaluating the results appropriately will always require a certain amount of knowledge. Tools that help harvest and make sense of data are not replacing human experts in the field, but they seem to be empowering a broader audience of individuals who hate-the-details in the realm of grasping machine learning.
The Architecture and Analysis of Structured Big Data
Schema informs search engines what your data “means”, not just what it “says”.
It may not be necessary to spend funds on assessing “big data” software for analysis. This can be used to improve your data science architecture. Being a buzz word related to computing power and storage capabilities, big data relates to a large dataset or a series of datasets that can yield critical insights about what site visitors prefer. However, it doesn’t often prove to provide a greater likelihood of successful analysis or give a digital marketer the ability to reach an actionable conclusion.
For years before “Big Data” became a part of our common language, statisticians were successfully sampling from product line data and using sampling tests to determine better marketing decisions. When testing, alpha and beta values can be really low with even small data samples (n>30) if the collection and research question is well-designed and planned. Today, your data has been so core to online businesses that even a small business needs to have connected data for effective SEO.
Whether you outsource your SEO or bring it in-house, your purpose for using “big data” or small samples of data should be first identified. Frameworks such as Hadoop, Cassandra, and Spark are readily available when your business has reached a state where modifying a lot of data sets makes sense. Businesses that have invested first in state of the art site architecture typically have the advantage of clearer data sets. Gaining the skills of a detail-oriented SEO or data scientist has become a direct competitive advantage for companies.
Data Services makes it easy for any business to evolve their approach to data by moving it to the core of their business. Everyone becomes successful: web developers love inventing new digital experiences, semantic data specialists can discover new and unexpected user trends, data architects can blend traditional data sources with new ones.
Technology Effects the Benefits and Costs of your Linked Data
According to the XBRL US Business Data Reporting Standard i.e the benefits and costs of structured data, “Data that can be unambiguously interpreted by a computer can be automatically”. Its mission is to foster the engagement of public business information in a standardized format. The technology chosen to structure your data could affect the benefits and costs associated with it. Make a solid company-wide plan so that it can be easily extracted, consumed and analyzed.
Some challenges businesses face for Google datasets, linked data, and the semantic web is adapting fast enough to new tools and processes. The development of rich enterprise knowledge graphs is where you can reap the benefits of linked data technologies by creating a standards-based knowledge model of your domain. It’s tougher for smaller or new business sites with a comparatively lower domain authority to win in SERPs when left to guess what content visitors want most. This emphasizes why a consistent process of reading your data can lead to quality content so in the longer run to provide the best information.
Mobile users consume online content differently from that read and sales attributed to desktop users. We’ve seen adaptive web design emerge with developers taking into account the different user patterns per device. As digital buyer expectations for easy content consumption continue to exceed current models, the most scalable means of attending to this is through semantically sound UX design and intelligent curation by machine data consumers.
Let’s start our discovery of just how your data can become this powerful.
7 Steps to Gain Data that Provides Immediate Value to Your Business
An overview list of what it takes to generate deep insights and deliver immediate value to your business with easy and accurate deployment:
1. TEAM PLANNING:
Start by using best practices in the architecture of your website. Design a detailed wireframe with a structured approach to prevent a “wing it” project.
Plan out a hierarchy before you develop your website. However, avoid problems that some IT departments incur by using the “waterfall” method of development. While it is great to specify absolutely everything, down to the point size of each font type, the line length of page headers and exactly how a simple photo gallery will work, SEO demands a flexible approach. And your data should serve to make continual improvements so that your site is not just about looking great, but works for users and drives revenue. Your business can benefit from more advanced machine learning insights on user behavior.
2. BUSINESS AND WEBSITE DEVELOPMENT:
Layout the contents of each web page in a logical manner that creates a great user experience. After planning the visual aesthetics for the project with foundational SEO best practices, it’s time to jump into code. Many core tasks are involved here such as building the site structure, constructing the templates, importing data, refreshing content, filling content gaps, and adding schema structured data markup, etc.
Stay closely aligned with your end business goals.
3. SITE AND DATABASE OPTIMIZATION:
To help search engines understand this content, mark it up with as much schema code as possible. If your site is being redesigned, all the code and data needed for your new site should be on the server ahead of time so that basic SEO optimization can be done in advance. To gain the benefits of new structured data SEO opportunities, plan now to have an SEO specialist continually add markup items.
4. AUDIT & FIX:
A technical data audit will ensure that digital marketers can overcome their top content creation barriers. It concludes that a data-driven process is vital** to publishing the right content that both the user and the business gains from.
We use technical audits that focus on specific aspects and provide regular monitoring to identify and mitigate a weak website organizational structure. We can help you review your Google Webmaster Tools data to upgrade the technical function and architecture of your website.
Leveraging Google analytics data and other data reports to glean user insights provides a path to future improvements. Metadata tells stories about your data. Over time, the value of good data increases. When a website is barely passable and offers no competitive edge, it may actually annoy customers, leave a negative impression and discourage return visits. Leverage critical insights gleaned only from your data to support user focused content.
A true data scientist will have skills like the ability to manipulate the data using something like a dask dataframe or array that can read columns of data in various formats, such as CSV. The real advantage is when it can be saved as a parquet file and used later for future out-of-core pre-processing needs. For many of us SEO’s Google 360 Analytics does a great job of providing deeper data reports than we previously had in our traditional Google Analytics.
6. USE ML: Native Graph Database for Machine Learning Pipelines
Data management of data warehouses shifts from reporting and analysis of historical events to driving business revenue planning.
It also provides insights needed to improve the customer user experience (CX) in real time and site performance. Going forward, expect to see a rise in the number of enterprises that start using real-time data and a ML pipeline to drive customized user experience at scale.
This is often achieved through native graph databases. A native graph database uses index-free adjacency to crisscross millions of data records with sub-second response times – even if those queries mine data several layers deep.
You can treat relationships between highly connected graph data elements as first-class database elements for machine learning pipelines. They build pipeline components that are complete with directional and quantifying properties used by the graph database engine.
“Augmented analytics will employ capabilities of AI and machine learning to automatically process data quickly and efficiently, cutting down the time it takes to transform data into insight. This means analysts will get to spend more time on data analysis rather than spending time preparing the data for analysis.
In 2020, data analysts will need to stay abreast of changes to local and international data protection laws by working closely with compliance teams and firms to handle personally identifiable information with care. With increased awareness of and emphasis on data protection, analytics tools will shift to focus on protecting the privacy and security of users’ personal data.” – James E. Powell, editorial director of TDWI
7. PLAN FOR ONGOING IMPROVEMENTS:
Finally, implement the right actions that your data reveals to better engage users. Sometimes training for your in-house team helps you go deeper into managing and creating new content, and to better familiarize themselves with their SEO data reports.
With semantic annotation, textual sources take on data attributes that machines need in order to organize, match, and serve web content accurately and efficiently. It is a leap towards revolutionizing the disciplines we use for data information management and better user knowledge.
Andre Valente, Technical Program Manager at Google urges SEO’s to dig into the data in your Search Console for your structure data implementation. It offers a trove of data reports that identify how pages are recognized so you can fix critical issues. By delving deeper, it is possible to see where your data is broken. The quality of your markup directly impacts your data sets.
So how do we go about gaining the secrets behind our site users and their actions?
Data Mining Reveals the Behavior of Internet Users
DIVVY HQ studied what it takes for a digital marketer to overcome their top content creation barriers. It concludes that a data-driven process is critical to business staying on track with thier business goals for growth.
He found that “64% reported that developing a comprehensive content strategy is a top challenge, while 46% said ensuring content ideas align with strategy is also tough. Rounding out the top 3, 42% of respondents indicated that it is keeping their data and ideas organized.
To tweak your web content for better product-based clustering, you must first get your mind around your user behavior preferences. Once your business has in-depth customer profiles created, it is easier to make predictions for what drives sales and on the lifetime value of each customer. Your structured data is core to forecasting what affects your subject line, the timing of new posts, where and when to publish news articles, the number of emails sent, if discount offers are effective, and more.
10 Benefits of a Data Science Approach
Solid Data Science practices empower businesses to leverage big data and gain benefits unobtainable without them to meet user intent with deep learning. You can:
* Boost user engagement onsite
* Lower churn rate
* Improve average click-through rates
* Drive conversion rates
* Increased revenue from marketing campaigns
* Beat your competition
* Innovatively find growth by unveiling new opportunities
* Help your company embrace efficiency and data integration
* Obtain a scalable production-ready infrastructure
* Increase productive collaboration
* If you have multiple locations, structured UX improves local Search marketing
Graph Databases and Analytic Systems
Conventional relational database management systems RDBMS are still the core of many businesses manage their data. However, the tabular structure of such RDBMS is frequently challenging some types of advanced analyses. It is one thing to capture and store data in the RDBMS, but logging and understanding the characteristics of the relationships between your key entities can be a headache. It is best to use a structured approach to content to increase user engagement.
Updating to a relational model like graph databases is one way to gain greater flexibility for analyzing modeled entities in light of their relationships. “Graph databases and analytics systems, based on the mathematical graph abstraction for representing connectivity, rely on an alternative approach to data representation that captures information about entities and their attributes and elevates the relationships among the entities to be first-class objects”, according to David Loshin of Knowledge Integrity. (das2017.dataversity.net/sessionPop.cfm)
A well-defined Data Architecture provides businesses with the ability to meet data volume challenges. It works as a footprint and guides for current and future data projects that support high-level marketing decisions. If needed, we can help you put a plan in place that will help you realize the full value of your website data.
Making the Right Decisions Based on the Right Data
As Google’s personalized search takes over a greater percentage of search results, SEOs are flanked with new challenges. For a long time, tracking a site’s rankings has been one of the core SEO KPIs, which relies on data accuracy. The new Google News Feed is also challenged with user location, analyzing previous searches, and browser history which impact the results users get. The scope of data accuracy itself is blurred in vagueness. Since personalized search now means endless SERP variations, per locations and how users toggle their mobile settings, the meaning of “accurate data” seems to be shifting.
It is becoming more challenging to feel confident that your ranking data isn’t cockeyed or misshaped by some form of personalization that you didn’t get factored in. How can your marketing team feel sure they are making the best decisions based on the accurate data?
SERPs vary for each user based on their location, which makes precise rank tracking harder – but it remains important. First, determine what your target locations are and then select a rank checking tool to track individual rankings. Whether you have one local brick and mortar business or multiple physical addresses, it is necessary to identify your target locations and tracking each of them over time to generate sufficient data to analyze and trust.
By conducting regular site audits, your business can make the right decisions with data-driven insights.
Infrastructure Design of the Future: Data-Centric Architecture
With more reliance on machine learning and artificial intelligence technologies, data has shifted from being regarded as important for informational assets to being at the core of innovation. It’s no longer enough to just be data-driven – organizations must be data-centric.
A recent survey led by MIT Technology Review at the request of Pure Storage reports that “an overwhelming 86 percent of leaders say data is the foundation for making business decisions, while 87 percent say it’s key to delivering results for customers”. Inside Big Data goes on to talk the challenges businesses face using data wisely in its May 24, 2018 post. 79% say ensuring the business is using relevant data sources is a challenge.
Present Key Concepts and Content First
Search bots and users don’t like it when it takes too many clicks to reach a page from your homepage. It is best to have horizontal linking structures or “thin site architecture” versus a deep vertical one. While an internal search function can be helpful, if users need to rely on it often, this becomes a clear indicator that getting further into the site or finding your content is a challenge.
Then check for extensions at a usable stage of development that assists in making this primary content widely available to search bots and machine learning.
• If you are in the automotive industry and have several local stores, use auto.schema.org to differentiate address locations for local searchers.
• For bibliographic resources and when needing the library sector, leverage bib.schema.org
• For the Internet of Things (IoT) try iot.schema.org
• Sites in the medical niche may source health-lifesci.schema.org
• Follow fibo.schema.org (a pending name) if your niche is the financial sector
Carefully consider the quantity and quality of your input data. Machine learning algorithms have huge “data appetites” often necessitating millions of data points to reach acceptable performance levels. Combined, with a data scientists’ biases in data collection, it may be substantially reduced. Many in the medical niche are spending mammoth resources to accumulate sufficient levels of high-quality, unbiased data to feed their algorithms, and existing data in electronic health records (EHRs).
The National Institutes of Health (NIH) says that “machine learning does not solve any of the fundamental problems of causal inference in observational data sets. Clinical medicine has always required doctors to handle enormous amounts of data, from macro-level physiology and behavior to laboratory and imaging studies and, increasingly, “-omic” data. The ability to manage this complexity has always set good doctors apart.” The September 9, 2016 article titled Predicting the Future — Big Data, Machine Learning, and Clinical Medicine stresses that in the end, data must be analyzed, interpreted, and acted on.
Blueshift ranks real-time and actionized data science as the number 1 growth marketing trend for 2017. “Data is the building block, the foundation, of customer identity and behavior that powers machine learning models and various marketing applications. As growth marketers, we’re looking to actionize this data faster and faster. Insights are great, however, we need ways to slice and dice the data manually and automatically to really affect our bottom line”, states Uruba Niazi.
The Semantic Web Constitutes Common Data Entities and Actionable Knowledge
Dr. Mirek Sopek, the founder of the digital solutions agency MakoLab S.A., is the presiding leader of its Semantic Web-oriented R&D division. Additionally, as the president of Chemical Semantics Inc., his experience is vast in the Semantic Web. At the Enterprise Data World 2017 Conference event, he enumerated on how it encompasses common data entities, actions, and the intersection of their relationships.
Today’s leading business site are finding a leading edge in search is possible through a deeper knowledge of schema.org opportune framework to build and deliver niche specific ontologies, a subclass of domain ontologies, and to epitomize concepts vital to a given industry’s schema. You can use schema.org across all domains, for any language.
As of today:
* It contains over 2,000 terms, 753 types, 1,200 properties, and 220 enumerations.
* Schema.org covers entities, relationships between entities and actions.
* About 15 million sites use Schema.org.
* Random yet representative crawls (Web Data Commons) show that about 30% of URLs on the web return some form of triples from schema.org.
Just as an individual product page needs proper nesting under a page containing all products sold, likewise your product schema needs to be organized and nested in a structured manner.
Data Science Architecture: Ingest, Process, Store and Analyze your Data
After reading several articles on the Google Cloud and Machine Learning Blog, I moved to my site to the Google Cloud Platform (GCP) earlier this year. I am on an exciting learning path gaining the first-hand experience about its use to gain better data to predict user intent. One article that inspires me, published February 21, 2017, by Lorenzo Ridi is Adding machine learning to a serverless data analysis pipeline.
It is a fascinating example of how one company, ACME, utilized several GCP components to create a serverless data analysis pipeline, and then mixed in machine-learning functionalities for sentiment analysis through a highly-consumable REST API. It adheres to a simple process that engages a common pattern in real-time data analytics projects. You will recognize this sequence of data flow: ingest, process, store and analyze your data.
It took months for us to choose from Amazon Web Services, Google Compute Engine, Microsoft Azure, and others. So far, we find that the on-demand scaling on the GCP is producing cost savings over our former web host while providing additional operating agility for our data crunching needs.
When deciding on a Data Science (DS) practice in your organization, even complex data environments still benefit from the Keep It Simple Stupid (KISS) methodology. While it is possible to run your data science architecture exclusively from your internal machines, at some point most businesses find that won’t scale to meet needs.
Dig into the data in your Search Console for your structured data implementation. It offers a trove of data reports that identify how pages are recognized so you can fix critical issues. By delving deeper, it is possible to see where your data is broken. The quality of your markup directly impacts your data sets.
Keyword Search of Relational Databases
We often rely on Ahrefs as it operates s two independent keyword indexes that help us assess large amounts of data for search marketing decisions. It’s a larger 4.6 billion data source of keyword indexes id ideal search phrases for studying search patterns of internet users when using its Keywords Explorer. Its secondary one consists of 429 million and is one of our go-to sources for researching which search queries that a site ranks for in organic search results, this time using Ahref’s Site Explorer.
Google Cloud Data Extended for Auditing Purposed
An August 21, 2017, announcement states that “the retention period for all Google Cloud Platform (GCP) Admin Activity Audit logs will increase to 13 months (from the current 1 month retention period) for any logs received on or after September 12, 2017”.
This retention increase is limited to GCP Activity Audit logs, which are found in the Stackdriver Logging and Activity Stream and available for a total of 400 days. “All other log types will continue to be retained for 7 days in the Basic Tier and 30 days in the Premium Tier”, the announcement states. Google’s intent is to make both data compliance and running audits easier. Activity Audit Logs on the GCP beginning September 12, 2017, will be stored and ready for 13 months of use in Stackdriver Logging.
If your business is asking the wrong questions from your data, precious time with limited hours in your workday to use your data effectively may be lost. Rather, empower your Google Assistant by answering the questions that consumers are asking most often. Your business can set the right KPI’s and know what questions to ask when analyzing results in mobile search. Hill Web Marketing ensures that so you find answers that are actionable, prioritized, and relevant.
Personalization and User Experience: Unlock the potential of your data, listen to consumer behavior and target unmet needs.
SEO and SEM campaign managers are typically deep into graphs, statistics, and detailed reports that help them conduct an analysis of data. Offering that to others in an easy to review manner helps everyone in an organization better understand Relationship Marketing.
With Big Data projected to drive enterprise technology spends to a new level near $242 billion according to Gartner, mining Query Data is here to stay, and as a result, more businesses of every size are getting into relational database data. To many enterprise-level businesses, machine learning models are a strategic asset. Every existing customer, business partner, vendor, transaction, defection, abandoned cart, bounced payment, and complaint can provide your business with a wealth of relationship-related data to learn from. From the perspective on the individual using the Internet, every typed or voice-activated request, every sale completed, product information requested, prescribed drug searched for, and environmental anomaly, is being tracked and built into databases graphs by someone.
Relational databases provide a commonly accepted means to store and access extensive datasets. Machine Learning Practitioner Jason Brownlee says, “Internally, the data is stored on disk can be progressively loaded in batches and can be queried using a standard query language (SQL). Free open-source database tools like MySQL or Postgres can be used and most (all?) programming languages and many machine learning tools can connect directly to relational databases”.
For an easier approach, he recommends first experimenting with SQLite. There are many examples and conversations on GitHub on how to read and manage data files. Or consider reaching out to Brownlee for computer science-related questions. What everyone does agree on is that your site’s composite architecture and the data you gain when someone visits it is growing in importance. It helps if you see your data in a new light; it is rich with insights on you can be more customer-focused.
Your Content Needs to Becomes More Personal and Compelling
The future of digital marketing is not like it was yesterday. Businesses must create a lasting and meaningful relational bridge with consumers that proves mindfulness of their shopping preferences and builds loyalty among their clientele.
Content is no longer simply words on a web page. Today, it must be richer, better than your competition, better prepared for visual search with items like videos, slides, meaningful white papers, infographics, etc. Then they can become entities that add richness to your Knowledge Graph.
We have evolved to a new form of content marketing that requires using personalized marketing technology to deliver the right content to the right person with compelling responsive web pages, mobile apps, in paid search Ad copy, and more. It is pretty tough to reach the ideal consumer at the ideal time with the ideal message without data science technology to get it done.
You can use your data to tell the future. No, it is not guesswork nor is it mystical. Semantic search, based on machine learning, is the contemporary science of finding consumer patterns and making predictions from data drawn from multivariate statistics, data mining, pattern recognition, and sophisticated predictive analytics. It lets you anticipate what your prospective buyers want and need even before they think of it.
Semantic Big Data Analysis
People, like you and I, probably many times a day, use contextual clues surrounding the words and phrases we read to better understand the implied or practical meaning of what is said. That’s semantic analysis (SA). As human beings, we do this intuitively, efficiently and often without a conscious effort. We sort out the context surrounding a word, phrase, image, or situation, pull out what seems most relevant, compare that to our past experiences, and use them to determine our take on the content at hand. Proper UX Design for e-Commerce resolves technical issues that may otherwise be missed.
Machines have historically performed poorly at this because they were deficient of that filter. They must decipher what is relevant and why a different way. Advances in Machine Intelligence and Natural Language Processing (NLP) have dramatically improved deep semantic analysis. Without our human intuition, they lean heavily on advanced algorithms, computers, and a lot of data collection and crunching.
The number of relational data patterns machines can determine and how well they can connect the relationships determines the relevance of the data. That relevance is both the goal and the unit of measure when it comes to Semantic Big Data Analysis. If your business can understand the content, intent, and user behavior at a deep level, you can provide more relevant content and thereby create a more resonant user experience.
Two Major Challenges for Reliable Linked Data
1. Business leaders often struggle to take the time to become sufficiently informed to understand the value of the latest advances in linked data technologies.
2. The cost of updating to newer data systems across large enterprises is often organizationally complex, time-consuming and costly. One has to find sufficient promise in a data upgrade to believe that real-life problems they are currently facing can be fixed by deploying linked data technologies.
A practical business application for pages with referential information – If your data shows that a great post is dropping from consumer interest, in the event of related breaking news, this content with referential information might be bumped from their original rankings in favor of breaking news. So updating your existing post or a page to include current information about what’s happening might be a good way to keep the post live.
Data Products created by Data Science Teams
Data products fueled by machine learning and Artificial Intelligence may source prevailing methods of solving users’ needs that outpace your competition. Give data science teams a role in your decision-making processes. This can balance and liven up different roles in the organization. Having data science leaders involved in product and business strategy discussions accelerates data product development. It creates a co-operative effort that successful semantic search strategies and implementationstrong> and visual search require.
”The lifecycle of a so-called “data product” mirrors standard product development: identifying the opportunity to solve a core user need, building an initial version, and then evaluating its impact and iterating. But the data component adds an extra layer of complexity. To tackle the challenge, companies should emphasize cross-functional collaboration, evaluate and prioritize data product opportunities with an eye to the long-term, and start simple.” – Emily Glassberg Sands on Harvard Business Review
“Data science in 2019 is fundamentally different from what it was before. Over the past year, machine learning has gone mainstream. Gartner forecasted the growth of predictive analytics by $1.1 billion in two years and scientists have been using deep learning algorithms everywhere from medicine to space exploration.” – Altexsoft
“Think of content writing as sowing seeds of collaboration, of business, of exchange. Think of text that communicates messages and values in a conversational, ready to engage in a dialogue way. Think of data as one of your best friends, think Vision vs Data.+ – Theodora Petkova
“Data is becoming the currency of marketing, and marketers will now have access to more data than ever…marketers will be able to use data to create more personalized and targeted products, messages, and customer engagements than ever before.” – Steve Fund, CMO of Intel
The bottom line is that you need to know what people prefer, their preferences, if your site’s architecture and categories helping them find solutions, and how to improve. It is called customer-centric marketing. We can help you explore your data in more detail and then design content to answer the questions they ask and that fit how algorithms link a search query to the best content.
If you need a proven set of services from a digital strategy and site architecture, Google analytic data mining set-up, data science, managed services and on-site training, Hill Web Marketing can help your company turn your big data challenges into tangible business outcomes.
Our experts can easily work in partnership with your development team to find advanced and valuable solutions that drive business outcomes faster.
Call Jeannie Hill at 651-206-2410. We love helping others build a more successful business. We can help you increase your online sales, attract more clients and Boost Your Revenue Profit Margins