Today I am continuing my coverage of Google Chrome integrations with Mattermark, a funding database. The Mattermark Chrome extension provides company and funding details on 1.3 million companies. The connector identifies companies based upon the current URL in the active browser tab. If the company is not found in their database, then users can request that the firm be added to Mattermark.
Content includes business descriptions, social media links, industry keywords, top executives with titles, and funding rounds.
The Chrome viewer also provides employee history, funding history, and Mattermark growth score graphs. Users can even trace along the graphs to find the employee counts, funding amounts, or growth scores over time.
A View Connections button opens up a LinkedIn tab and displays the top connections to the company. To improve connection accuracy, Mattermark passes the LinkedIn company code. Other social media links include Facebook, Twitter, Angellist, and CrunchBase.
The extension provides quick access to company searches, saved searches, and lists via the Mattermark browser. If the user clicks on the company name, the full Mattermark company profile is displayed in a new tab. Additional content includes funding and acquisition histories, similar companies, news headlines with open web links, and user notes.
Multiple graphs are displayed including Growth and Mindshare, web traffic, social media (Twitter, Facebook, and LinkedIn), and app ranks (Apple App Store and Google Play). Unfortunately, none of the graphs provide context. Thus, if there is a spike in social media activity, there is no direct method for discerning what events or promotions drove the spike. Likewise, a declining growth score lacks context.
Users can also click on one of the company investors listed in the Chrome window to view a list of other investments made by the PE/VC. Along with a list of the investments with recent funding data and growth scores, Mattermark provides investor analytics including top industries, growth momentum, business model (B2B vs. B2C investments), stage distribution, and company location. Thus, a startup could use Mattermark for both building prospect lists targeting fast growth companies and research on potential investors. Knowing what stage, industry, and location a firm invests in helps narrow a CFO’s funding targets.
Mattermark collects and edits its company dataset. After cancelling the last of its data licenses last year, Mattermark now mines all of its data. They also have a small team of analysts for verifying the extracted content.
Executive information is limited to top names and titles. They do not provide contact information. However, users can click on the LinkedIn icon to be taken directly to the LinkedIn company page for research.
I should warn you that the service is expensive. Users must have a paid subscription or free trial for access to Mattermark Chrome extension content. Pricing begins at $6,000 per annum.
Given its price and limited coverage, Mattermark is best suited for PE/VC firms and startups looking for their next funding round.
Over the next few days, I will be covering various Google Chrome integration tools for sales intelligence. Yesterday, I discussed the Zoominfo extension and today I’m covering DataFox’s implementation.
DataFox just launched a sales intelligence service after previously focusing on the PE/VC space. Thus, their underlying dataset covers fast growth companies. The Chrome integration, along with their SFDC implementation, is part of their value proposition.
The DataFox Chrome integration recognizes URLs and provides company profiles to subscribers. Content includes the DataFox Score, sizing data, URL, year founded, a business description, and ten similar companies. The tool is specific to company URLs and does not recognize companies in other contexts such as LinkedIn. If a company is unknown, the app allows the user to quickly request the company be added to the database.
The DataFox score is a composite score which assesses the firm’s financing, human resources, and momentum. According to DataFox, their score “uses machine learning to quantify hard-to-define traits like financial stability and management quality, and most importantly, how those traits can predict a company’s growth.”
Clicking on the company name takes the user to the DataFox company profile. From there, the user can send the company information to SFDC.
Similar companies are displayed with a similarity confidence score, logo, and DataFox Score. Users can click on similar company names to view the similar companies.
Similar companies are identified using a proprietary algorithm which finds peers based upon sector, size, news co-mentions, participation in conferences, and company keywords. According to DataFox, “out of the thousands of keywords in our database, any given company will list only about a dozen. To deal with this, we want to be able to harness a measure of similarity between keywords. After all, if company A lists ‘cloud storage’ as a keyword, then we should have more confidence they are related to a company listing ‘file sharing’ as a keyword than a company listing ‘mobile payments’ as a keyword.” Similar companies can be executed against both individual companies and lists. Thus, a sales rep can take their top client list and clone a set of comparables.
Over the past few months, a number of vendors have added Google Chrome extensions to their service. Initially, this trend was focused on the technology vendors (e.g. HG Data, DiscoverOrg, Datanyze), but extensions are now available from a broader set of sales intelligence vendors (e.g. Zoominfo, Unomy) and funding databases (CB Insights, DataFox, and Mattermark). Typical features include mini-company and executive profiles, contact prospecting, contact enrichment, send to CRM (usually SFDC), and technology profiles.
I have selected four connectors for mini profiles beginning with Zoominfo’s new ReachOut service. In the coming days I will also profile DataFox, Mattermark, and DiscoverOrg.
Officially launched last week, Zoominfo ReachOut provides quick access to contact information from Zoominfo and LinkedIn contact profiles. When the Chrome browser displays one of these profiles, the Zoominfo logo begins pulsing in the Chrome browser bar indicating that contact information (email, direct dial, and company phone) may be purchased for one credit.
Users can then send the contact to SFDC or email it to themselves.
A separate browser view provides access to purchased credits as well as contact searching by a combination of company name, contact name, and title. This feature does not require the Chrome browser.
The service has several big gaps in its initial release:
It fails to recognize company websites or LinkedIn company profiles (unlike other vendors, their focus is on the contact, not the company)
While it supports LinkedIn, it does not recognize LinkedIn Sales Navigator
ReachOut provides no company firmographics within the Chrome contact profile. Users must go to the ReachOut browser view and then mouse over the company name or click on the hyperlink to view company information.
As such, it operates as an enrichment and purchased contact tracking platform.
Trialers receive an initial five free credits while community members (freemium users that share their mail contact file with Zoominfo) receive ten free credits per month. ReachOut subscribers receive ten, twenty, or thirty credits per month with credits costing two dollars each. Zoominfo does not indicate whether bulk credit purchases are available or whether credits may be shared across a team.
UK social selling vendor Artesian Solutions recently incorporated in the United States and opened a Boston office. The firm has also signed several data partnerships which allow them to expand coverage to 25 million companies and 65 million people. The firm did not indicate the names of their partners or geographic distribution of the coverage.
The firm has already signed five US customers from the banking, finance, and technology sectors. To manage US operations, founder Mike Blackadder has relocated to Boston. The firm has also hired Tom Beriau as their new Vice President of Sales based out of Boston. Tom has over twenty years of enterprise software sales experience.
Artesian Solutions provides mined news and sales triggers from the open web along with a social media view (Twitter and Blogs). Their core offering is the Artesian Social Intelligence SaaS platform which supports company and prospect research on the web, within CRMs (SFDC and MS Dynamics), and via the mobile browser (iPad).
Artesian supports 25,000 users in the UK. Clients include Adobe, American Express, Barclays, Cisco, Citi, Deloitte, HSBC, KPMG, and Siemens.
“Incorporation sends a clear message to our US customers about our intention to provide support for our award winning service on a global basis,” said CEO Andrew Yates. “Our largely multi-national customers many of whom are headquartered in the US, have been driving demand for Artesian – regarded by them as more than just smart data and insights about customers but a complete sales engagement solution which complements LinkedIn. Think of Artesian providing engagement smarts for companies and markets is the same way LinkedIn does for people insights. Our new office in Boston is just the beginning of our expansion strategy, but it will ensure we are perfectly placed to capitalise on this glaring gap in the market, and provide our customers with the solutions they are demanding, everywhere they operate.”
Boston was selected as it offers “a great base from which to service customers across the whole of the US, as well as a stepping stone towards delivering a truly global offering.”
Boston is also the home of several other sales and marketing intelligence companies including Avention, D&B NetProspex, and Zoominfo.
The firm claimed that it is growing at 100% per annum. Unfortunately, they did not provide the growth metric (i.e. revenue, customers, users).
Artesian posted an impressive 94% retention rate last year.
Last June, Artesian closed an $8 million Round B. The round was led by previous investors Octopus Investments and Kreos Capital raising total funding to $11.2 million. The funds were dedicated to UK infrastructure growth, international expansion, and ongoing investment in mobile and social apps.
In December, the firm released a calendar-linked mobile app called Ready which helps sales professionals prepare for meetings by providing the latest news, insights, and financials to sales reps. The new service “demonstrates Artesian’s commitment to product innovation and developing cutting-edge mobile applications that help strengthen its customers’ brands and accelerate their sales success.”
The app, available at no charge to Artesian customers on the Apple and Google stores, also supports team collaboration and information sharing. According to Artesian, “Ready allows users attending the same meeting to collaborate and share insight on the go, ensuring that they never miss a thing. Ready delivers tangible commercial outcomes – the pre-sales meeting ritual will never be the same again!”
In the 2016 edition of my Field Guide to Sales Intelligence Vendors, I am planning on expanding coverage to UK products. This will include profiles of DueDil, Bureau van Dijk (BvD), and Artesian Solutions. Avention, which has strong offerings for US, UK, and global sales teams, is covered in my 2015 edition.
DataFox, which has focused heavily on providing investors with information about growing technology companies and financings, entered the sales intelligence space with the launch of DataFox for Sales. The move is reminiscent of CB Insights which launched a sales product last September. DataFox has been messaging about its sales and marketing use case since July and formally launched the sales service this week along with a Salesforce.com connector.
DataFox claims the following benefits for sales reps:
75% less time to discover a prospect’s needs and draft insightful emails
54% more companies reached with this increased productivity
34% increase in response rates
DataFox CEO Bastiaan Janmaat argues that executives are flooded with cold calls and irrelevant emails. “This is largely being driven by the universal adoption of marketing automation software and the sharp increase in inside sales reps over the past two years. It’s just too easy to send cold emails today, and most of them are not personalized. “We are changing the game when it comes to enhancing the sales and buying experience.”
DataFox for Sales’ primary information presentation model is lists. While companies can be viewed individually, the product focuses on company lists maintained by DataFox, marketing teams or sales reps.
The DataFox platform is, therefore, well positioned for Account Based Marketing (ABM) and Account Based Sales Development. It has strong list management tools for ABM targeting and sales alerts concerning ABM prospects and accounts.
Lists can be built via traditional prospecting, uploaded as CSV files containing the name and URL, or constructed from one of several lists or tools:
Conference Lists: Conference lists are a unique feature of DataFox. They can be used by sales reps for pre-conference / trade show planning to determine which competitors and partners are sponsoring the show, exhibiting, or presenting. They are also useful for trade show planners in evaluating potential shows (are our competitors and peers attending?), demand generation managers for assembling account based marketing (ABM) targets, and business development executives for identifying potential partners or licensors.
Similar Companies: Similar companies are identified using a proprietary algorithm which finds peers based upon sector, size, news co-mentions, participation in conferences, and company keywords. According to DataFox, “Out of the thousands of keywords in our database, any given company will list only about a dozen. To deal with this, we want to be able to harness a measure of similarity between keywords. After all, if company A lists cloud storage” as a keyword, then we should have more confidence they are related to a company listing ‘file sharing’ as a keyword than a company listing ‘mobile payments’ as a keyword.” Similar companies can be executed against both individual companies and lists. Thus, a sales rep can take their top client list and clone a set of comparables.
Curated Lists: Curated lists include published lists (e.g. Inc. 5000, Deloitte Fast 500), lists built by DataFox analysts, and lists shared by DataFox users. Because these lists are matched against the DataFox database, they contain additional firmographics and insights which may not be available in the source lists. Sales and Marketing can use these lists for ABM targeting, identifying growth companies, or extending into new verticals. Users may also share lists publicly or with team members. Users can even collaborate on a list with team members adding or deleting companies from a list.
Prospecting provides an unconventional but compelling list of variables:
Keywords: Keywords are employed in lieu of traditional industry coding. Keywords are quite granular. For example, adtech included eight options. DataFox should look at merging some of these codes. For example Push Notification, Push Notifications, and Push Notification Services provided different company lists.
Location: City, State, Postal Code, Country,Country, and International Region. While a few pre-canned metro areas are avalailable, MSA, County and Radius Search were not supported.
Investor: PE or VC investor
Exit Status: Checkboxes for Private, Public, Acquired, Subsidiary, Product, and Closed Down.
Headcount: Employee Range Finance: Revenue Estimate, Total Funding, Latest Funding Round Amount, Funding Stage, Latest Funding Round Date, Market Cap, P/E Ratio
Date Founded: Year Range
Scores: DataFox, Growth, Finance, Marketing, HR
Marketing: Twitter Followers
Uploaded company lists are automatically matched against their database and enriched. An Insights feature provides quick list segmentation analysis. This view includes top keywords, top investors, headcount distribution, funding distribution, and top locations. Thus, a marketer can quickly upload a list of or trade show booth or webinar attendees to assess the value of the tradeshow or for tweaking the messaging of a webinar.
List layouts are customizable so additional DataFox variables may be added including additional Datafox scores, web traffic time series data, twitter followers, awards, and funding data. Datafox even allows users to add editable columns (e.g. Notes, Follow up Actions and Dates, and calculated variables).
Any list may be filtered using DataFox’s standard prospecting variables. Lists can also be downloaded as CSV files. Unfortunately, CSV files do not maintain currency formatting and lose leading zeroes in ZIP codes. This is a common problem for vendors that support CSV but not XLS formatting.
It is this list-centric model that most differentiates DataFox for Sales from the traditional sales intelligence competitors.
When run against curated, user saved, and conference lists, the Insights Report includes Top Keywords, Top Investors, Top Locations (city), Quarter over Quarter Funding (Historical bar graph), Investor co-occurrence, and Top Five Companies (by DataFox score). There is also a bubble chart called Comparative Trajectory which graphs the DataFox score against the Year Founded with bubbles sized according to the number of employees.
DataFox does not provide company and executive counts, but they indicated that their company universe exceeds one million firms and that they are adding 500 companies daily. DataFox mines its data from
10 different funding databases
3,000 conference lists
10,000 curated lists such as Deloitte’s Fast 500 and the Inc 5000
40,000 News Sources
SEC (EDGAR) Filings
Online Profiles (Social Media, Websites, etc.)
Social Feeds (Twitter and AngelList)
Company data includes company descriptions, DataFox Score, location, employees, funding history, keywords, social media links (e.g. LinkedIn, Facebook, Twitter, Angellist), corporate blog link, news, executives, conferences, acquisitions, investments, similar companies, top public lists, and user lists. Much of this information is not available in traditional sales intelligence tools but is visible via a one page overview. Additional company views include people, news, and similar companies.
The DataFox Score is a proprietary score which sums four subscores: Financing, HR, Influence, and Growth. Here is how they describe the scores:
At DataFox, our mission is to provide data-driven business insights to salespeople, marketers, analysts, executives, and investors. One of our proprietary systems is the DataFox scoring system, which uses machine learning to quantify hard-to-define traits like financial stability and management quality, and most importantly, how those traits can predict a company’s growth.
Using the companies in our database, we’ve developed models to provide a leading indicator of success. This predictive method is particularly important when evaluating private companies, where traditional factors like revenue and past performance paint an inaccurate picture of future growth.
To accomplish this, we’ve built a series of algorithms to evaluate companies based on growth, influence, finance, management and overall quality. These scores allow DataFox users to quickly search our company database and identify the best or most suitable companies in any sector, location, or stage. Our scores identify the best companies, just as Google’s PageRank algorithm identifies the best webpages.
It should be noted that while the Datafox scores are valuable for qualification and prospecting, they are not predictive scores. Information is based upon historical data and not matched against a company’s ideal profile. Nevertheless, if a firm is targeting fast growth companies, particularly those that are funding candidates, the scores help identify firms on a growth trajectory.
Users may follow a company by clicking a track button and adding the firm to a current or new list. Users may also store private notes with profiles or share comments with their team.
Unfortunately, the company profile does not include an export feature for sharing with team members or reviewing when offline.
Contacts include title and a LinkedIn search button.. The LinkedIn searching was more effective than most simple name searches as DataFox passes the LinkedIn company number to assist with the search. Users may also purchase emails and direct dial phones using credits. No biographic information or executive news is provided.
News is categorized into the following categories:
Although the number of categories is limited, DataFox intends to expand the number and breadth of trigger categories. Triggers are identified by natural language processing (NLP) and machine learning. A subset are flagged as key articles and sent to their team of thirty editors for validation and summarization. Here is an example of the summarized news story (“curated highlight”) for Artesian Solution’s Series B:
Triggers and summarized profiles are viewable within the product and as email alerts. Users can email items to team members (or to clients and prospects) and post them to Twitter. The fox icon allows reps to share notes about the item to their team.
Product Marketing Director Arik Pelkey believes their sales triggers are an area of strength due to the quaility of their natural language processing. “Our supervised algorithms are improving all the time and our unique approach to sales triggers is a big differentiator for us.”
DataFox includes a Salesforce.com connector with I-frame viewing on account and contact pages along with “stare and compare” updates. Within the I-frame, reps can view the one-pager, people, news, and similar company tabs. DataFox also offers bulk de-duplication and cleansing of SFDC accounts.
If the CRM connector has been deployed, prospecting lists display filterable flags indicating current customers and prospects. Users may also send individual companies, company lists, and executives to SFDC. When uploaded, companies are added as accounts and execs as contacts or leads. There is also a two-click company add from company sites to SFDC. This feature requires users to install a Google Chrome Add-in.
Overall, the UI is clear and well laid out with fast site response. Key qualification variables are easy to identify and actions are clearly labeled. A search box provides type-ahead suggestions for keywords, companies, or lists. A dynamic left hand icon bar expands when the user mouses over the icons providing easy access to the home page, key features and lists, tutorials, and recently viewed items. When mousing over a company name, a baseball card profile is displayed.
Because DataFox is coming from the funding world and utilizes a different presentation metaphor, there are some significant differences between DataFox and traditional sales intelligence services:
DataFox offers fourteen day free trials. Pricing was not disclosed, but it is seat based and subject to volume discounts.
DataFox has released a very strong 1.0 release. They provide some compelling features not found in other products which make it a worthy offering for sales teams focused on fast growth companies and marketing teams looking to assemble ABM target lists. As with all 1.0 products, there are functionality and information gaps. In the case of DataFox, they lack public company financials, family trees, technology data, and executive screening.
However, with ongoing investment DataFox could become a credible competitor to the incumbent sales intelligence vendors. In the meantime, companies focused on fast growth companies and ABM sales and marketing should check DataFox out.
Late last month, Avention announced availability of OneSource DataVision, a hosted marketing platform which integrates internal and external customer intelligence. By matching Avention company and contact data against customer and prospect files, Avention improves the accuracy and firmographic fill rates of marketing databases. The result is improved customer segmentation and targeting based upon enriched data from Avention’s Global Live database of companies and contacts.
DataVision also provides analytics and visualization tools for marketers. “As a result, you will be able to identify and leverage key customer and prospect segments to make more informed decisions, identify cross-sell opportunities, key industries, verticals and much more,” states Avention.
“In a world where gaining new customers has become more complex and competitive, and customers engage with vendors later in their buying processes, marketing and sales teams need to align their data more than ever. OneSource DataVision is a powerful – yet easy-to-use – tool that helps marketers understand their current customer bases in detail and identify the most relevant target companies and segments,” stated Lauren Bakewell, SVP of product for Avention. “Initial customers have seen positive business impacts and results from their use of OneSource DataVision.”
DataVision provides a centralized marketing view of customer data which may be housed across multiple platforms including CRMs, Marketing Automation Platforms, and order entry systems.
“Companies need accurate, deep data to gain the marketing intelligence needed for better targeting and advancing customer relationships. The ripple effects of greater marketing intelligence within the enterprise are improved sales cycles, cost of lead and sale and revenue generation,” blogged Jennifer Nash. “OneSource DataVision helps marketers increase the value of existing customer and prospect data by centralizing, analyzing and visualizing multiple data sources.”
DataVision includes a gap analysis tool which assesses the total addressable market in order to identify underserved markets and growth potential. After enriching and segmenting the data, DataVision users can prospect for similar companies.
As DataVision provides ongoing cloud based data cleansing and standardization, It is likely to be competing against similar offerings from ReachForce, Zoominfo, and D&B NetProspex. The service is also likely to butt up against predictive analytics companies such as Lattice Engines and Infer. While Avention offers a set of predictive tools (e.g. Business Signals and Ideal Profiles), they do not appear to be fully integrated into the initial release.
Analyst David Raab noted that DataVision’s hosted data model is likely to result in fresher data “since any query to DataVision will return the latest information available to Avention.” He also complimented DataVision’s visualization tools and the platform’s ability “to compare those distributions with the entire Avention universe of known firms.”
A significant trend over the past two years has been the blurring of the lines between sales and marketing with sales intelligence vendors addressing marketing requirements (e.g. DataVision, Zoominfo, InsideView for Marketing) and marketing functions migrating down the pipeline to sales reps (e.g. SalesLoft Cadence, Salesforce IQ). Historically, OneSource shied away from building marketing tools in order to focus on the sales and research functions. While they long offered match & enrichment services, this offering was managed by their Professional Services team as either a custom project or CRM enrichment. DataVision is their first product designed specifically for the marketing team. Of course, improved data quality at the top of the funnel provides benefits to sales reps in the form of improved lead quality, enriched leads, and properly routed opportunities.
Social media job site Glassdoor recently published its second annual ranking of the top jobs in America and, of the top twenty-five jobs, ten were in technology. The top ranked position was data scientist which jumped from ninth last year. Other high ranked positions were Solutions Architect (#3), Mobile Developer (#5), and Product Manager (#8). Glassdoor bases their rankings on three variables: the number of job openings, salary, and career opportunities rating.
The Median Base Salary for a data scientist is $116,840. Other tech base salaries can be seen in the above graphic.
When Network World interviewed data scientists about their position, they noted the pleasure of discovery as a key benefit. A common complaint amongst data scientists was the headache involved with data preparation. “At times, munging [parsing] through data can get tedious,” said data scientist Jeff Baumes at Kitware. “The worst times are when I realize the quality, quantity, or other aspect of the data simply prevents me from gaining the level of insight that I hoped to gain from the data.”
The McKinsey Global Institute found there is a growing shortage of analytics talent in the United States. By 2018, they projected a shortfall of 140,000 to 180,000 professionals with analytical expertise. They also projected a deficit of 1.5 million analytics trained managers and analysts.
Data scientist talent acquisition and retention are a significant problem for organizations, particularly amongst firms looking to initially establish data science capabilities. In an article in the MIT Sloan Management Review, Ransbotham, Kiron and Kirk Prentice found that 55% of analytically challenged firms had a problem recruiting and retaining analytical talent while firms described as innovators had much less difficulty. Only 29% of innovators reported difficulty recruiting with 24% reporting difficulty retaining. Innovators also are much more confident that they have the appropriate skill levels in house. While 74% of Innovators believe they have hired the appropriate analytics talent, only 17% of the analytically challenged felt the same.
One advantage of partnering with sales predictive analytics companies such as Lattice Engines or Leadspace is the ability to bypass hiring of in-house data scientists and instead work with their resources and tools. While it is still important to understand the results and train staff in data interpretation, much of the complexity is removed.
Furthermore, the strategic advantage accruing to analytics capabilities is declining as more firms develop such capabilities. In 2012, 67% of surveyed respondents believed analytics capabilities conveyed a strategic advantage. By 2014, the percentage had dropped to 61%. The authors posited two reasons for the decline: an increase in the number of firms investing in analytics and a difficulty in converting analytical insights into business action. Half the respondents noted difficulty in translating insight to action.
“Technology is no longer the main barrier to creating business value from data: The bigger barrier is a shortage of appropriate skills,” said Ransbotham et al. “Companies with appropriate analytical skills are far more likely to say that analytics is creating a competitive advantage in their organization than are other organizations.”
Jim Fowler, who founded three crowdsourcing startups (Jigsaw which was acquired by Salesforce.com and renamed Data.com, InfoArmy, and ), was asked how crowdsourcing has changed over the past decade. His observation was broader than crowdsourcing and applied to any tech company looking to gain mindshare:
I think they change in the same way that we all have. We all are just overloaded with information. Getting people’s time and getting them to pay attention is much more difficult now than it was back in the beginning of Jigsaw for sure. Getting journalists and analysts to talk and write about you is different because there’s so much going on. In fact a lot of the big publications don’t even exist or don’t write about it anymore.
It’s become much more flat, if you will. More players in it, so that’s interesting, but I just think the biggest thing is just people … There’s so much stuff flying around out there now that really making sure you have a crisp clear message so that they understand the value is even more important than it ever was and that’s just been the big change. People are more sophisticated, they’re more … They know how to use data and I see that trend continuing.
Fowler also noted that Owler combines crowdsourcing and semantic mining with editors. While machines can do much of the work around event aggregation and structured alerts for exec changes, M&A, and funding rounds, editors ensure that information is properly tagged and mapped. While this editorial review of news introduces a short delay in information delivery, it reduces the number of false positives and passing mentions of companies. Furthermore, it allows them to de-dupe the stories and accurately capture M&A and funding content.
Basically, it solves your signal to noise problem through the addition of a short editorial review step.
If you just used technology to try to do this, you would get a lot of noise in there because really it’s a lot harder than it looks to figure out that the article is actually about Apple. Apple gets mentioned in millions of articles. To know that it’s actually about Apple is … To just do it with technology is really hard. What technology can do is say, “We think this is an article about Apple and we think it’s an Apple acquisition and we think this is the company that they did and we think this is it,” but what you need to do is create a task that gets prioritized very highly that a human looks at really quick. Checks out all the data and goes, “Ah, that’s right. We’re good,” and then sends it on to the people.
Otherwise you get a lot of noise, what I’m getting at is that technology can get you way down the road, but you need humans to get you all the way down the road if you want high quality data.
It is this multi-process approach that is likely to be the future of data collection and aggregation. Traditional methods of data collection via phone interviews or analyzing filings information are quite expensive while semantic mining can get tripped up on context (is this about company X? Is this a relevant story? Is this a discussion of current events? Is this an actual event, proposed event, or mere rumor?). Likewise, crowdsourcing requires a very large audience to obtain the wisdom of the crowd and works best on easily defined fields such as address, phone, and email (i.e. Jigsaw contacts). Crowdsourcing also works well at gauging sentiment. For example, Owler captures sentiment around whether the CEO is doing a good job and the projected fate of private companies. But crowdsourcing does a poor job around complex information such as industry code tagging or corporate linkage. It is through complementary methods that vendors will drive qualify forward while keeping data costs in check.