The Road to Becoming a Data Driven Company

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Today we discuss the 4 tiers that drive a data driven company via the "Data Pyramid". Included in this pyramid are key performance metrics, data warehousing , analytics and business intelligence and data science. Tune in to see how each of these tiers can play a role in helping you measure the success of your business.

Transcript of The Road to Becoming a Data Driven Company

  • F R A M E D DATA The Road to Becoming a Data Driven Company
  • How Do I Create a Data Driven Company?
  • A lot of companies these days like to say that they are a data driven or metrics driven company.
  • But what does that even mean? Are they really data driven?
  • Data driven can cover a huge range of goals and abilities.
  • So then, what makes a company data driven?
  • First step: Define your companys Key Performance Metrics (KPM)
  • These are the hard, quantitative numbers that are attached to your business goals
  • For example, an e-commerce site might measure revenue per day While an online social gaming company might prefer to measure daily active users or monthly active users
  • Figure out what makes your company tick and what drives its success. After defining your quantitative goals, you can align your entire companys culture and team around it.
  • A lot of companies will hire data scientists, data analysts, and product managers without necessarily figuring out the metrics that define your business success.
  • Second step: Data Warehousing (storing your data)
  • Data Warehousing is taking all of your production data (from web pages, queries, user behavior, demographics, etc.) and storing them into one centralized source of truth.
  • Data Warehousing is taking all of your production data (from web pages, queries, user behavior, demographics, etc.) and storing them into one centralized source of truth. The schema (organization) of your data warehouse will be centered around your KPMs. i.e. If youre tracking daily active users, it might make sense to have a user login table.
  • (Yes it sounds a bit like database 101, but youd be surprised to see how many companies skip that step)
  • Third step: Now we can get into Analytics and Business Intelligence
  • Now that you have a data warehouse, you or your analysts can use the information to determine answers to key questions. How many users logged on in the last 7 days? How many people logged on from Idaho on Tuesday night? How many women above 40 bought a virtual good from our online game?
  • The next goal is to extract numbers and generate hypotheses, reports, and dashboards to figure out where we want to go next.
  • We did it!! Many companies can stop here and pretty much be a reasonably data driven company.
  • However, in the last couple of years weve rediscovered machine learning and have made it part of our everyday arsenal.
  • Fourth step: Data Science
  • Roughly, data science means taking features and variables from the hypotheses we defined earlier in analytics and BI, and using these to craft machine learning models to either explain our past or predict the future.
  • Machine learning models inform product decisions, strategic decisions, and form the basis for a curiosity based company that is now on the offensive. How can we use our data to monetize the company further? How can we use our data as a key asset? How can we use our data to figure out what our users are doing?
  • (BTW, Framed Data provides painless data science services that any company can use to increase their user retention. Give our free trial a shot and click the logo below!)
  • USER-FACING EXAMPLE #1: amazon.com
  • Can you spot the machine learning here?
  • Can you spot the machine learning here?
  • At the bottom is a section labeled Customers Who Bought This Item Also Bought
  • At the bottom is a section labeled Customers Who Bought This Item Also Bought Given what Ive bought in the past, they use machine learning to try to predict what I might be interested in buying in the future.
  • At the bottom is a section labeled Customers Who Bought This Item Also Bought The main KPM they are trying to optimize for is revenue.
  • USER-FACING EXAMPLE #2: LinkedIn
  • Can you spot the machine learning here?
  • Actually there are 2, but well cover this one first.
  • This is a 300x300 box that tries to predict people I might know.
  • LinkedIn is optimizing for added connections and friends, getting their users further linked in.
  • The articles in the news feed are also based on my interests and previous clicks.
  • The goal here is to optimize for engagement.
  • But how do I determine the best KPMs for my business?
  • Picture the business funnel for an e-commerce website.
  • At the top you have all the people visiting your site. SITE VISIT
  • Out of those users, some may look at an item. SITE VISIT ITEM VIEW
  • Then out of those, a few might add the item to a shopping cart. SITE VISIT ITEM VIEW SHOPPING CART
  • Finally, you have people checking out, which translates to revenue. SITE VISIT ITEM VIEW SHOPPING CART REVENUE
  • In each stage of the funnel, you have people leaving. This is the attrition rate of the funnel step. SITE VISIT ITEM VIEW SHOPPING CART REVENUE
  • Each stage is a potential Key Performance Metric. SITE VISIT ITEM VIEW SHOPPING CART REVENUE
  • Given your business, whether its online or brick and mortar, there are various steps that a user may take ultimately generate revenue.
  • Its up to you to figure out what drives your business.
  • H I @ F R A M E D . I OFramed Data