What are 360-degree views of an enterprise?

Accessing holistic knowledge across four core processes:
Customer 360, Employee 360, Product 360, & Market 360.

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When you have a 360-degree view of an object, you can see everything around it from all angles. On a larger, professional scale, when you have a 360-degree view of your enterprise, you can understand your business from all contexts and practices. This business concept is most commonly taking shape in the form of Customer 360, where companies like Salesforce are driving solutions that unite teams and data to have a holistic understanding of their customers.

While Customer 360 on its own is seemingly taking off, it is considerably stronger with the support of other 360-degree views – namely of other fundamental departments of a business. Along with customer insights developed through Customer 360, these departments are focused on insights developed towards an organization’s market, its employees, and its products, which altogether, form a business strategy called Enterprise 360.

Though Enterprise 360 is a relatively new name on the market, the methodology around it is built off of the longstanding belief that the more insights a person is given about their business, the better they can make decisions and boost customer satisfaction. Baked into a powerful knowledge graph that serves as an umbrella to connect all structured and unstructured data, Enterprise 360 grants you 360-degree views of your business in 4 crucial aspects: Employee 360, Product 360, Market 360, and Customer 360.

Why do we need 360-degree views? The current challenges.

Many organizations experience a myriad of problems that are brought on by having to maintain extensive amounts of diverse data and documents. The following are a few of the most common issues:

Data silos

For most organizations, data silos are commonplace simply because they are the unfortunate natural result of having many departments in an organization. Typically, marketing stores their data in one place, product management stores theirs in another, and so on and so forth. Within these different storage systems, there are also vastly different ways to build and manage the data; marketing may use mostly text documents and slide decks, and product management may rely on spreadsheets. The obvious problem here is that none of this data is being shared even though different departments could benefit from it.

Perhaps the marketing team would like to run an analysis on products that have garnered the most customer attention and support, or maybe they want to understand how the product itself works in order to create content for product campaigns. How can the marketer do this when all the information sits in the product management silo that is sectioned off from them? As a result of data silos and a lack of sharing, employees on average spend more than 25% of their time searching for the information they need to complete their tasks – effectively wasting productivity and money.

Multilinguality

In international organizations, multilinguality, while an asset in most regards, is a hindrance specifically for data management. Documents are in different languages and even numerical spreadsheets rely on linguistic labels. In many cases, organizations who have offices across the world may need to pour their resources into translating material that is necessary to develop company-wide reports or projects.

Too much data, not enough knowledge

This problem, though very common, is often not thought of because organizations are not totally aware of the distinction between data and knowledge. Organizations who do well in maintaining data do not necessarily mean that they do well in producing fruitful knowledge or insights about that data. A company may have hundreds of the neatest spreadsheets that are visualized in clear graphs or charts, but if there is not someone or something making connections between the numbers and their application to business scenarios, the hundreds of spreadsheets are not very effective. While the hard data is important, they are not always valuable until they have been transformed into knowledgeable and actionable insights. Context is important in this case, where employees can make decisions based on meaning that is explicit in the data.

The major thread across these common problems is a lack of centrality and interconnectedness.

Broken down into parts, they mean this:

Data silos = critical information is spread and sectioned off into too many places

Multilinguality = no universal or central vocabulary is being used across a database

Data with no knowledge = different data points and business objects are not being connected to generate valuable insights

When things are not being linked together, whether through synonyms on a linguistic level or through repositories for data and content management, they simply just sit. There is no enrichment, there is nothing to learn from, and the potential of these objects are wasted because they’re still in a rather basic stage.

What’s the solution? Link & learn with Enterprise 360.

Recently, we’ve seen a growing interest towards 360-degree solutions that can help to foster necessary connections between data and people to move past this basic stage. Years of experience with customers has shown that the more access and insight a person is given into their organization’s processes, the more they can make better business decisions and ultimately boost customer satisfaction. Enterprise 360 follows this model, which says that if we place a comprehensive knowledge graph over all our data sets and departments, we can link data objects and derive meaning from them. In one way, we can manage parts of the data that are getting too overwhelming to look at simply because it’s just too much, and in another way, we can uncover valuable knowledge that was hidden deep within our data.

Why a knowledge graph for Enterprise 360?

It is very easy to get lost in a spreadsheet that contains rows upon rows of data, and they do not really begin to generate insights until the data has been visualized via comparative charts, graphs, etc. Even more, these spreadsheets, while effective for numerical data, do very little for unstructured text or multimedia-based data, which makes up the bulk of organizational data.

Knowledge graphs link and visualize data, content and topics to be integrated henceforth as a ‘context engine’ in various applications, e.g. search engines or recommender systems.

Data silos, which are the main problem that resonate with organizations, are effectively eliminated with the knowledge graph because they link all these silos together. Looking like a web or map of sorts, the knowledge graph literally connects pieces of your database – Customer 360, Employee 360, Product 360, and Market 360 – together along with its content, thus serving as the basis of 360-degree views.

Under these 360-degree views, users of the knowledge graph can identify meaningful relationships within their data, as well as eliminate data inconsistencies.

Enterprise 360

Supported by this enterprise knowledge graph, Enterprise 360 considers how different aspects of an organization can be linked together to gain holistic access to business processes and other key components. Enterprise 360 touches on some of the most relevant and crucial aspects of an organization — that being its people, its customers, its products, and its markets.

These foundations often function as separate departments, though they consistently rely on each other to be successful. Enterprise 360 means to connect these foundations – Customer 360, Employee 360, Product 360, and Market 360 – into one interoperable landscape so that they can help each other as they are intended to.

This image shows how the 4 pillars of a company connect to make Enterprise 360.

Customer 360

The Customer Life Cycle

The customer life cycle indicates all the stages customers go through during their engagement with any business. As indicated in the image, the customer life cycle is usually composed of six stages: awareness, consideration, decision, purchase, retention and advocacy. Customer 360 helps you to get an overview and control your customer life cycle so you can generate the best customer relationships and results.

With Customer 360, you can successfully monitor each of these stages in your comprehensive knowledge graph and engage with its key components. In the awareness stage, for example, a key component is that you understand your customers’ language and which channels work best for them. In order to do this, you first need to conduct some research on the market and what your customers are saying about you. You might harvest public comments left on your social media or website reviews, or you could also keep up to date with industry influencers and the responses left on those influencers’ pages. You could use this information to create a controlled vocabulary in your taxonomy which serves as the starting point for your knowledge graph. These crucial insights along with your company’s own internal content can help bridge the gap between your company’s language and your external market’s language, so that you can determine the best way to speak with and target your audience. Using Customer 360, similar practices can be made for each of the remaining stages, where you can combine key information from multiple sources into one comprehensive knowledge graph that maps these relationships together.

Personalize customer experience with Customer 360.

Suppose you work for an online retail company who specializes in selling furniture. You want to suggest relevant pieces to your customers based on what they’ve previously purchased. Using a knowledge graph with Customer 360, you can track customers previous interactions and make ads that are geared towards pieces that make sense. In other words, if your customer previously purchased a table, your intelligent search platform will target an ad for a chair instead of yet another table.

Read the white paper >

Employee 360

The Employee Life Cycle

The employee life cycle covers the entire relationship between an employee and their organization. It comprises the following stages: attraction, recruitment, onboarding, development, retention, and separation. Having 360-degree views on this life cycle ensures that all of the employee’s needs are met because all aspects of their hiring process and retention are mapped and visible. The 360-degree knowledge graph can help HR managers and existing team members manage the employee life cycle all in one place.

Develop valuable employee journeys with Employee 360.

Suppose you are an IT company who wants to hire a new product programmer to the development team. As the HR manager, you need information from the development team to get input for the role description, budget information from finance to know how much you can list the salary for, and your own HR expertise to write the role description and post it to the best channels. This requires going through many steps and people in order to be successful.

Using a knowledge graph supported by Employee 360, you can link all this relevant information together: you can import external research you have conducted about average salaries into your knowledge graph and compare it to your company’s budget, you can map the necessary skills and compare them to incoming CVs to see if the candidates match your needs. Once you have hired an employee, you can continue to build on the knowledge graph by adding onboarding information such as relevant passwords and sites, training materials etc. The point of this exercise is to have a knowledge graph that acts as a knowledge hub of sorts, that both employer and employee alike can access to manage the life cycle and facilitate continuous enrichment in their time at the company.

Read the white paper >

Product 360

The Product Innovation Life Cycle

Gaining a 360 degree view of a product, not only includes how the product is developed, but also its journey as it moves along the product life cycle, ensuring a constant feedback loop and overview is being shared with the development side in order to ensure product longevity. The product life cycle, as we have defined it, includes product development, product introduction, product growth, product decline, and product maturity. These 360-degree views of a product form the basis for continuously improving the service and maintenance processes around a product, creating end-to-end solutions for customers that can also be optimized from an end-user perspective.

Drive innovation with Product 360.

Suppose you are a pharmaceutical company looking to develop and launch a new drug. The drug development process requires many arduous steps and painstaking review so that the drug is effective, safe, and compliant. Throughout the entire process, documentation is very important but hard to maintain. With the Product 360 knowledge graph, users can map all these documents and data so that potential inconsistencies are visible and all the relevant makers of the product can see how documents are connected.

Read the white paper >

Market 360

Competitive markets and audiences

In any industry, but particularly in highly competitive ones, product markets are incredibly difficult to navigate. To make the right decisions about market positioning and communication, market professionals need to know what their competitors are doing, which possible alternatives to their products and services are out there, and which new technologies are being created in their industry. Obtaining competitive intelligence, keeping up with market trends, tailoring information to different stakeholders, and excelling at omnichannel marketing are extremely important to organizational success. The 360-degree knowledge graph can help market professionals map all the relevant pieces of the market together in order to deliver engaging marketing messages at the right time and place. Holistic knowledge of a market enables targeted value propositioning to specific audiences and stakeholders while being able to stay ahead of the competition.

Gain competitive intelligence with Market 360.

Suppose you are a company who exists within a very competitive space like the tech industry. Thousands of products, companies, influencers, etc. are being born each day, so it is imperative that you stay updated in order to remain competitive. Using Market 360, you can chart what people are talking about by automatically extracting and analyzing information from influencers, publications, research papers, social media, news, and so on.

Read the white paper >

Want to hear more?

Check out our webinars about Enterprise 360 & Knowledge Graphs.

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The Key to a Successful Digital Transformation

Watch our presentation and panel discussion featuring Semantic Web Company and Enterprise Knowledge.

Experience now

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5 Use Cases & 10 Steps To Get There

See how you can build knowledge graphs that help you streamline organizational success.

Experience now

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The Art of Developing Valuable Employee Journeys

Watch how our speakers from Walmart, RGP, and JobTeaser benefit from Employee 360.

Experience now

Development steps towards Enterprise 360: Crawl, Walk, Run, & Fly

As a comprehensive, interconnected methodology, Enterprise 360 requires that you put together various “features” of semantic technology. If we consider Enterprise 360 as a toolbox of sorts, each of these features resemble the different tools found within your box. The enterprise knowledge graph is perhaps the greatest tool, but in order to build a knowledge graph, you first need to build things such as a taxonomy, metadata, a recommender system, etc.

The advantage to working with PoolParty to achieve these holistic, 360-degree views is that you can have all these tools and stepping stones in one software.

Crawl

A fundamental activity to building an enterprise knowledge graph and ultimately achieving Enterprise 360 views, consists of creating taxonomies and ontologies for your organization’s data.

In a broad sense, taxonomy is the method used for classifying data and content into relevant groups and hierarchies. A taxonomy is similar to a library catalog where in any given library, you can retrieve a book based on how it is classified: author last name, book genre, and so on.

An HR business taxonomy can classify objects similarly: high-level concepts such as Job Roles, Office Locations, and Skills remain at the top of the taxonomy → under Job Roles you can organize departments Sales and Programming → under Sales you can organize the specific roles Account Executive, Sales Representative, and Customer Experience Manager.

This setup proves to be very helpful for the taxonomy user, because it means that if they want to retrieve information about the Customer Experience Manager, they only have to click through this logical chain of elements to do so. The taxonomy’s hierarchical representation of business data gives it structure so that it is easier to navigate and understand.

The ontology successfully leverages its close relationship to the taxonomy so it can produce meaning that could not be found otherwise. In short, an ontology gives you the ability to express the precise meaning of entities and their relationships to each other. If we look at this same example of the HR business model, the ontology would show an HR Manager that a Job Role has an expressive relation to an Academic Degree. Here, we can discern that if the HR Manager is trying to hire a new person in the sales department, the candidate must have a degree in order to qualify.

Walk

Once you have established your fundamental building blocks in the taxonomy and ontology, you can move on to the next steps which include auto-tagging and named entity recognition. Within your taxonomy, you can add labels to concepts and terms which serve as “tags” to your data.

If you are an ecommerce retailer selling clothing, for example, you might organize your taxonomy into Summer vs. Winter clothes. The next level contains Winter Coat, Sweater, Snow Boots. For each of these concepts, you can tag them with bundled synonyms, multilingual terms, definitions, etc.; in our example here, a Sweater can be described with alternative names and languages – all the information shown in the highlighted box serve as the tags.

The advantage for you with these tags is that they create rich metadata that will improve your website’s ability to return relevant results because it grabs for the meaning associated with the tags. Since these terms are all bundled together with the tags, if your user types “Jumper” into your search field, they will get all results pertaining to the words “Jumper,” “Pull-over,” and “Sweater” instead of being limited only to products that contain the exact word “Jumper.”

This image displays how tags work in the taxonomy and thesaurus server.

Named Entity Recognition is another step in the “walking” process that helps companies achieve Enterprise 360. Most, if not all, companies collect hundreds of thousands of documents containing large amounts of text made up of natural language. This unstructured data is incredibly hard to organize and analyze because the machines cannot interpret natural language well on their own. With cases such as ambiguity, for example, the word “apple” could mean a different thing depending on the context: the sentence “i stick with apple for all my tech needs” is understood by humans as Apple like the company because we can make these logical jumps based on background knowledge. A machine, however, does not immediately know or understand the context so it may assume that apple refers to the fruit in this case. Only when we combine machine learning and natural language processing techniques with knowledge graphs, can we enable text mining processes that better analyze text by not only processing words, but understanding the underlying concepts and their context.

An entity extractor and natural language processing can help you pull out all the key terms in your documents and make sense of them, which can help you in a variety of ways such as research, contract analysis, etc.

To see how these capabilites work in a front-end application, check out our free Entity Recognition and Contract Intelligence demos.

Entity Recognition

Contract Intelligence

Run

The standard search functionalities of a website typically do not support complex queries. This means that the results that users receive are often limited based on a small range of filters or keywords that are not being enhanced with natural language processing. Semantic recommender systems are the powerful alternative to standard search for their ability to suggest smarter results based on the user’s interactions with a platform.

Suppose you are a travel company whose website uses standard search functionalities. A customer of yours has just booked a train trip from Vienna to Milan, and your website automatically recommends other dates or trips for the same route even after booking. The website knows it should display some sort of ad to persuade more purchases, but in this case, additional train tickets are counterproductive because it is highly unlikely that the same customer will purchase the same trip again. The advantageous next step in this case is to recommend car rentals, excursions, and hotels in Milan, which a semantic recommender system can do because it has been trained to make enhanced “pairings” based on user input and activity.

A standard search engine can only recommend items based on simple filters and direct experience with the platform; the crucial difference is that semantic recommender systems can make intelligent recommendations using the rules you have defined in building the recommender system and all the interactions your user has had on the search platform.

Question-answering systems are an additional step that elevate your search platforms. In the same case as the semantic recommender system, a standard search engine is very limited to retrieving results based on exact keywords and filtered interactions. A question-answering system is proving to be a very powerful asset in the business world because it can respond to questions asked in natural language, thus improving overall search performance and customer experience.

Customers of Amazon’s Alexa can speak the sentence, “Alexa, what’s the weather today?” and Alexa, built off a question-answering system can respond in the same conversational language. Internally, an HR Manager can use a question-answering system to type in the question, “Who can program in Java and uses Linux?” and the system will retrieve all the relevant employee profiles in the database. The key here is that the system can read, understand, and respond to conversational text – ultimately allowing users to have more precise, tailored searches without having to worry if they’re using the correct language or specific keywords.

To see how semantic recommender and question-answering systems work in a front-end application, check out our free HR Recommender and QAnswer demos.

HR Recommender

QAnswer

With these types of search engines powered by semantics, only then can you truly begin to run and transition into the fly stage which consists of the prized Enterprise 360.

Fly with Enterprise 360

All these steps are supported by the enterprise knowledge graph which culminates in the Enterprise 360 views. From the building blocks of taxonomies and ontologies, the enterprise knowledge graph is the representation of all the terms, concepts, and relationships that were defined. The knowledge graph is further enhanced through text mining and entity extractors, which serve as the basis for natural language processing techniques that develop complex search tools. The knowledge graph is combined with advanced semantic capabilities and machine learning to make intelligent pairings in the recommender system and smart searches in question-answering systems. What you get with all this is an advanced set of tools that only help you to build internal processes that ease tedious workflow, search platforms that better the customer experience, and a comprehensive, holistic view of all these systems working together.

Enterprise 360 is the result here, where you can have all these benefits in one interconnected lens that allows you to have a rich understanding of all your moving parts. Using Enterprise 360, you can remove common data inconsistencies since all your data and content is linked together in one knowledge graph that can help make any errors visible. The practice of having 360-degree views over your data epitomizes data governance, which is a set of principles and standards that ensure your data remains at the highest quality that you can manage. When you have access to all sides and angles of your organization, only then can you really gain control of your assets and make informed decisions.

Being informed about all the key foundations of your company is paramount, and why the methodology of Enterprise 360 exists.

Customer 360, for example, helps you understand your customer to provide them a tailored service and experience. Employee 360 allows you to have strategic HR processes that help you and your employees navigate the employee life cycle and produce fruitful workplace journeys. Product 360 compiles all necessary product documentation and industry demands to ensure that you can quickly deliver quality, innovative products to your stakeholders. And finally, Market 360 allows you to keep up with ever-changing trends and develop strong messaging that you can disseminate across the right channels.

Altogether, these views comprise the ultimate picture with Enterprise 360 – eliminating data silos and increasing data governance that only gets better over time. With the right roles, workflows, and responsibilities in place, you can highlight all the gaps that exist in your data to ultimately make your business smarter and all your relevant stakeholders satisfied.

Interested in learning how you can fly with Enterprise 360? Talk with an expert to discuss how PoolParty could be the right fit for you.

Discuss Enterprise 360