As we discussed in my previous articles, we identified where opportunities existed throughout our eDiscovery process where we can directly impact our bottom line and contain our total cost of review.
To recap, we first identified how targeting and narrowing our scope of collections reduced our review costs by up to approximately 30%. Secondly, we identified how an early investment in an ECA to reduce our cull rate by a few percent led to cost reductions of up to approximately 15%. Lastly, we identified how investing in advanced analytics, such as active learning, led to significant cost reductions of up to approximately 30%.
Individually, we can see that each one of our choices prior to review choices led to notable savings over the life of the case. In today’s exercise, we will look at back at all the oppurtunies we previously identified and the choices we made. This leads us to our final question: What happens when we put it all together?
Establishing the Numbers
Let’s recap the assumptions we will continue to work off of. In this scenario, we have 6 devices with costs of $500 per device and each device has 50 gigabytes of data. Assume each gigabyte contains 1,000 documents, and our reviewers work at a rate of 50 documents per hour at $50 an hour.
Lastly, our processing rate will be $35 per gigabyte, and hosting will be $25 per gigabyte, per month. We will also be using a four-month lifecycle for this case.
Putting it all Together
So what happens when you put it all together? If we narrow our initial collection, investing in culling the data better and leverage active learning, What’s the net impact of that in economic terms?
First of all, we have to acknowledge that there will be diminishing returns. When looking at each choice individually, it will have a greater impact since it is the only thing affecting our data set. But when done simultaneously, the individual impact will be reduced since we are leveraging each choice on top of the previous one. Think of it as taking a percent of a percent.
The “Perfect” Workflow
So let’s start in Month 1 start. We will perform our targeted collection, collecting 4 devices instead of 6. This will result in our initial cost savings of $1,000 and some reduction to our hosting and processing costs to come.
In Month 2, we will invest $5,000 upfront for an ECA and take a closer look at our data set. As we previously discussed we expected to increase our cull rate by 5% to 85%. Our hosting and processing will still be slightly reduced compared to the “Standard Workflow”, but our Month 2 total costs are now slightly higher with our “Perfect” Workflow. Don’t be deterred just yet, remember, our real goal is to reduce our total costs.
Let’s look at Month 3. We now make our $3,000 investment into analytics expertise upfront to use active learning technology. Similar to Month 2, our recurring costs stay the same and are lower than the “Standard Workflow”. But as we can see in the chart below, our incurred costs are continuing to increase and it would appear as if the “Perfect” Workflow is driving more expenses than savings. This doesn’t seem like a very convincing exercise does it?
The End is in Sight
Now it’s time to reap the fruits of our labor. Month 4 is the finish line we have been working towards. So through each month, we initially reduced out costs by $1,000 with our targeted collection, incurred $5,000 of costs with an ECA, and then an additional $3,000 of costs utilizing active learning. Net additional costs of $7,000. This might seem high now but is small in comparison to our total costs.
So, let’s move into our review phase and look at how our initial investments played out. So in this example, we would have reduced our total cost by approximately $34,000. You may also notice that there a range present on the chart. Realistically, it’s hard to gauge exactly what each investment affect on our review costs will be, but even at the expected high-end of the range, we still see cost savings of approximately $20,000.
The Key Take Away
And so, my message to everyone is that strategic workflows can provide a significant amount of budget efficiency beyond just line-item reviews, reductions. So certainly, if you want to reduce total cost of review, and you can get down to 90 cents per document reviewed instead of $1 per document reviewed. That’s going to reduce your total cost of review, certainly, but we can only squeeze that lemon so much, and there’s not a lot of juice left in it. For the most part the industry has gotten about as low as it can get on almost all of the line items to continue reducing TCR. So, by identifying strategic opportunities throughout the life of our project we can continue to seize all possible cost savings.
And so as we think about discovery in 2021. The key takeaways is, now is the time to evaluate your opportunities to reduce the total cost of review, which ones you currently utilize and which ones you have the potential to implement. We identified 3 effective ways to do so, and they all don’t need to be utilized, but just by adopting one of these approaches you are going to see significant results pretty quickly.
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Acorn is a legal data consulting firm that specializes in AI and Advanced Analytics for litigation applications, while providing rigorous customer service to the eDiscovery industry. Acorn primarily works with large regional, midsize national and boutique litigation firms. Acorn provides a high-touch, customized litigation support services with a heavy emphasis on seamless communications. For more information, please visit www.acornls.com.