Wrap Up from Women in Analytics Conference 2018

Wrap Up from Women in Analytics Conference 2018

The second half of the Women in Analytics Conference continued with break-out sessions and networking opportunities. As the attendees settled in for the rest of the day, three common points were heard across all of the discussions:

  • finding a purple unicorn that possesses all the skills needed in data science is improbable; therefore, you need to look to assemble a team
  • the educational discipline in which you studied is often irrelevant, what is relevant is curiosity and the ability to collaborate
  • data science/analytics team are often repurposed from other roles and responsibilities and nascent teams need to put together a new identity and look for quick wins to shore up their legitimacy as a new organizational entity to prevent individuals from getting pulled into traditional work

Many of the sessions in the afternoon and the keynote during dinner focused on starting, building and keeping teams together. Sandy Steiger discussed the importance of focusing on re-investing in staff so that they stick with the company. With data scientists and analysts in such high demand, she pointed out that it’s hard enough to attract talent, but keeping talent is a wholly different endeavor.

One of the most interesting things to emerge with bringing so many different types of organizations and roles together is that we were able to network and find out how different companies had structured teams and in which department they resided. The common theme in this conversation is that the data analytics or science team(s) are often odd ducks sitting in a traditional IT department, but marketing has not yet made the leap to see how they should do more than liaise with the team. Many data professionals felt that they were an island unto themselves and it poses the question if we’re not seeing the org chart about to get disrupted with the data teams becoming the center of a hub-and-spoke organizational model because they are closest to the customer.

The implications of data science and the adoption of AI and machine learning are so vast that the conference ended with the invitation to get more involved in the community. It’s clear that this conference and working group is here to stay and Columbus will be the host city for a tremendous number of meeting of the data minds from here on out.

March 16, 2018

Top Takeaways from Part 1 of #WIA2018

Top Takeaways from Part 1 of #WIA2018

A sold out crowd for this year’s Women in Analytics Conference provided quick evidence of what an exciting time we’re experiencing. The attendee base encompasses a wide variety of titles, job roles, company sizes and educational backgrounds giving further credence to the cross-functional nature of the profession.

Robin Davies, Director of Data Operations, Global Data Insights & Analytics for Ford Motor Company started the day with explaining how Ford is using an analytics-first approach to rethink their customer journey. The insights coming from their analytics are helping the organization rethink not only product roll-out strategy, but also pricing strategies. One example given was that if they can understand their customer better to view them from a more lifecycle approach, then this can be fed back into their pricing model to offer lower price points to entry with increased confidence as to the total lifetime customer value. Davies also spent time explaining the enormity of data and how much data is really there to harness with all of us still really being at the very beginning of the process.

From the sessions we attended in which each speaker surveyed the room to understand the breakdown between developers, data scientists, analysts and functional business roles, there was a pretty even spread across the board. One session, in particular, hit the bulls eye in terms of cross-cutting techniques and applications and that was on word embedding. Bijaya Zenchenko of HomeAway blew everyone away with word embedding and the many different techniques and approaches readily available. We walked away wondering why are organizations still spending so much time manually trying to understand the digital customer journey and not pouring their money into an application that can scrape, aggregate and accelerate insights?

The lunchtime panel provided different viewpoints from women in analytics working in academia, functional leadership role and data engineering to hear first individually their work in the analytics’ field. Each presentation was unique with the common theme being that analytics is a commitment and defining it is paramount to achieving any sort of success. The caution towards understanding data bias came up repeatedly and each speaker was in agreement that we will not be overtaken by bots anytime in the near future. The panelists additionally agreed that the culture of an organization is a make-it-or-break-it element to analytics adoption and appreciation.


March 15, 2018

Expeed to Attend Women in Analytics Conference

Expeed to Attend Women in Analytics Conference

The upcoming Women in Analytics Conference that will be held on March 15th at the Columbus Convention Center is the talk of many Slack channels and Twitter feeds. As an initiative of the Tech Community Coalition, the event is expected to be exponentially larger than previous years due to the growing commitment to make Columbus the data analytics capital of the country.

Expeed Software is proud to send two of our staff to support the analytics movement and support growing the number of women practicing advanced analytics and data science. With key speakers from top firms in Columbus, the sessions cover a wide range of artificial intelligence, machine learning and security topics. Additionally, the conference includes sessions on career pathways and ample opportunities to network with like-minded people.

Plan on following @expeedsoftware on Twitter this Thursday for our live tweet coverage of the event with an in-depth synopsis following the event.

March 12, 2018

Key Takeaways from Day 1 Gartner Data & Analytics Summit

Key Takeaways from Day 1 Gartner Data & Analytics Summit

Day one of the Gartner Data & Analytics Summit was packed with sessions and vendors of all sizes who are offering software products to help data scientists do less programming and more analysis. The consistent theme of the first day of the conference came down to three key takeaways:

Key Takeaway #1: Advanced Data Analytics is Just Getting Going
While there is a ton of buzz about the promise of advanced data analytics, the analysts were consistent in their assessment that the industry is still in its infancy. One key stat that stuck out was that it’s estimated that 70% of the work involved in applying machine learning algorithms revolves around data preparation. A new class of software applications called Augmented Analytics is coming to market with the aim to automate data preparation to speed up the process of analysis. As any data scientist will tell you, the biggest hurdle to analysis is getting the data prepped in a way that creates a complete set so that you can then slice and dice as you see fit. Companies like DataRobot, ClearStory Data and Paxata are bringing new tools to market to help automate the messy stage of data prep that everyone likes to gloss over, but is the key to good data science.

Key Takeaway #2: Artificial Intelligence is Accelerating in Adoption
Gartner cited that 10 years ago AI was known as a sci-fi movie (and even old then), but that in the last 10 years, AI is starting to crop up everywhere. With more and more availability of AI, it is being plugged into applications to help accelerate product adoption, product longevity, increase safety and more. In fact, once you start thinking from the digital transformation viewpoint which centers on the customer, it’s hard not to start brimming with ideas for where AI could add value in either obtaining needed information from the consumer to allow the product to perform better or sending information that would be useful to help the consumer perform better. This two-way communication between entity-to-consumer holds so many possibilities that product innovation is anticipated to exponentially soar as devices and applications get re-wired from an AI framework.

Key Takeaway #3: Innovate or Consolidate
With the need for agile development and time to market being critical elements of market success, the pressure for companies to innovate is intense. The market share of the usual giants of IBM, Microsoft and Oracle are under constant attack from smaller competitors that are faster to market with innovative new products. With such disruption in the marketplace, Gartner anticipates that there will be a wave of new start-ups, but that consolidation is inevitable and larger conglomerates will emerge.

March 6, 2018

5 Ways to Get Started Down the Path of Predictive Analytics

5 Ways to Get Started Down the Path of Predictive Analytics

You might have heard the term predictive analytics and thought “that’s what I’ve always wanted”!!! For most marketers, this is the case and you blame the IT group for not being able to provide the dashboard and reports to show you what you know the data is trying to tell you. While true marketers have a highly attuned sense of intuition and insight, better outcomes will also result when we don’t skip steps. Here’s five ways to get started down the path of predictive analytics that will help you work better with your IT team and bring about long-term results:

1) Minimze What You Want to Measure
Whaaaaa???? I finally have all this data and you’re telling me to minimize what I want? (Please note that I did not say simplify, but minimize–big diff). Many marketers drive business analysts and IT personnel crazy by wanting to have everything on a dashboard or available to query because of FONHI (fear of not having it). This is inherent in marketers and makes many of us equivalent to data hoarders who are scared that we might not be able to undertake the segmentation that we want at the exact moment that we want it. While painful, listen to your IT partners and focus on what it is that you really need to measure that will drive the business forward.

2) Get the Right Data
You don’t need all the data, but you do need the key data as we’ve previously stated. The likelihood that all key data is in the same database is next to nil; moving to an enterprise architecture where it doesn’t matter where the physical location of the data lives is key. Organizations will increasingly need to move to modern data architecture models that focus on tying systems together making the movement and aggregation of data the primary concern and not the database architecture.

3) Prepare Data for a Predictive Analytics Model
Data can only be properly analyzed when it can be joined and segmented in multiple different ways. You need to ensure that your data is clean, proper data hygiene practices are followed and that it is structured in such a way that you can easily access it, move it and measure it.

4) Putting Processes in Place for Using a Predictive Analytics Model
Ensuring that staff understand the importance of capturing and cleaning data for use in analytics is key. Reviewing all entry points of data across the customer lifecycle from the viewpoint of analytics is a useful exercise to identify all of the areas where data is incomplete, erratic, overly restrictive and identifying processes that cause friction to the user can help showcase which processes are in need for an overhaul.

5) Use the Right Tools
Predictive analytics is typically a new endeavor for organizations and requires different technology products and methodologies to maximize success. Ensuring that your organization has a strong technology partner that has the expertise to consult on both the business and technology fronts will allow your organization to expedite its digital transformation. While the right tools are a necessity, the know how is the critical element in the world of predictive analytics.

March 1, 2018

The Intersection of CX & Digital

The Intersection of CX & Digital

Google dubbed it the “Zero Moment of Truth” to explain that point at which you realize that the customer, not you, is completely in control. Perhaps we were fooling ourselves all along to think that we did have control of our ordering process, inventory, distribution, customer service and accounting and the truth is we never really did. Alternatively, we could surmise that we lost total control the day that mass penetration of mobile occurred and consumers started doing their own research on our brands–without our involvement.

Regardless of when this ZMOT occurred at your company, we can all agree that nothing will ever be the same. Consumer attention spans are inversely correlated to the product options now available online for purchase. In fact, studies now cite that consumers have an attention span of less than 4 seconds which is below that of a goldfish. What’s worse is that consumers are finicky and will typically spend even less than 4 seconds on your website once they have arrived there from an enticing ad or other promotional hook. The consumer has a seemingly unlimited number of possible products, the power of information and moreso, the power to inform others about their own experiences.

This new reality means that corporations must now shift their viewpoint from one of being internally striving to push products out to a distant market to one that begins and ends with the customer. The movement of shifting one’s entire worldview along with the associated business processes and technological infrastructure is what is typically referred to as digital transformation.

Now that the customer is self-empowered through the ability to find information and product reviews online, the need to understand the customer is at an all-time high. We need to be able to study the customer’s every digital move and take action at the exact right time in order to gain our next sale. We need to understand what the customer is telling us through digital so that we can provide similar products and services that match their online sentiment. This need to not only capture, but predict, what the consumer may do next is at the cross-roads of customer experience and digital.

Given this lens, the intersection of customer experience (CX) and digital is far easier to understand because it breaks down to simple economics. My organization needs as much information as possible about consumers so that we may take the right action at the right time to supply the right product. Digital is the fastest, most measurable, most economical way and typically, preferred channel for consumers to research and make purchases. My conclusion becomes that my organization needs to overhaul our thinking about the customer journey and leverage digital to gain insight.

When you get to this conclusion and you are ready to start taking action to understand how your organization needs to look to support a digital and customer-first operational model, it’s time to contact Expeed.

March 1, 2018

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