1 item tagged "re-engineering"

  • Growth Stories: Change Everything

    mobile-uiInterview by Alastair Dryburgh

    What do you do with a small technology company which has an interesting product but is stuck in a crowded, noisy market where larger competitors have locked up many of the distribution channels? You could keep struggling on, or you could make a bold move; re-engineer the product to meet a different purpose for a different market. That's what Pentaho did, leading to 6-times growth over 5 years and a successful sale to a large organisation.

    In this interview their CEO Quentin Gallivan describes how they did it.

    Alastair Dryburgh: Quentin, welcome. This series is about that period of a company's evolution when it has to go through the rather dangerous territory that lies between being and exciting new start up and being an established profitable business. I'm told that you've got a very, very interesting story to tell about that with Pentaho. I'm looking forward to hearing that.

    Quentin Gallivan: Okay, great.

    Dryburgh: What would be useful would be if you could give us a very quick background sketch of Pentaho. What it does and how it's evolved in the last few years.

    Gallivan: So Pentaho, the company is approximately 12 years old. There were five founders, and they all came from a business intelligence technology background. What they were looking for was a different way to innovate around the business intelligence market place.

    One of the things I saw going on with that company was that the biggest challenge in companies doing data mining or predictive analytics on unstructured data or big data, was how do you get all this unstructured data, and unstructured data being clickstream data from websites, or weather data, or now what's very popular is machine data from Internet of Things devices.

    I wondered, is there a company out there that can actually make it easier to get all this different data into these big data analytical platforms? Because that was the biggest problem we had.

    When I looked at Pentaho, at the time it was not that company. It was not the new, sexy, next generation company, but I knew the venture capitalist behind Pentaho. We spent about a month just talking about what could the company be. Version one of the company was really a business analytics software product sold to the mid-market. They got some initial traction there, but that was a very cluttered market - very busy, a lot of noise, lots of large incumbents with channel dominance and then lots of small companies. It was hard to get above the din. I was not interested in Pentaho as the company was, right? I didn't see that as very interesting, very compelling.

    What interested me though, was when you dug deeper on the technology I thought it could be repurposed to address the big data problem. That was a big leap of faith, right? Because at the time, Pentaho wasn't doing any big data, didn't have any big data capabilities. The customers were all mid-market, small companies and it was known as a business intelligence company.

    Dryburgh: Pretty substantial change of vision really, isn't it?

    Gallivan: Massive, massive change, and I looked at it and I spoke to the VC's and said, "I would be interested in taking the CEO role, but not for the company that you've invested in, but for a very, very different company and I think we can do it. I don't know if we're going to do it. It's a long shot, but if you're willing to bankroll me, and allow me to build a team and support the vision, I'll give it a go."

    Dryburgh: Could I stop you there a moment to see if I could put a little bit of a frame around this? You've got a pretty fundamental change here.There's probably, very crudely, three different elements you've got to look after. First is obviously the technology and I guess that must have needed to evolve and develop. Then you've got what you might call the harder side of the organisational change, the strategy, definition of who the customer is, the organisation, the roles, the people you need, that's the second one. Then the third element which I think is particularly interesting is the softer side which is the culture. I'd be really interested to hear which of those was the biggest issue for you?

    Gallivan: That's a great question. I like the way you framed it, I would add a fourth dimension, which is the market perception of you. How do you get people to stop thinking about you as Open Source BI company for small and medium size businesses and think about you as leading, big data analytics platform for a large companies, for the large enterprise. Those are the four vectors that we needed to cross that chasm.

    The hardest one was not the culture because at the time, the company was very small. It had 75 employees and we are going to be over 500 employees this year, right? At the time it was really an open book from a culture... The founders were very open to a change in the business. For most startups, less than 100 employees, the culture is generally driven by the founder or founders and so there was no resistance.

    Dryburgh: Okay, good. So what were the biggest things you had to do to make the transformation work?

    Gallivan: If you look at those, just think about the transformation in those four key areas, you look at the metrics. Five years ago we were known as a commercial open source BI company selling to midsize companies. What we wanted to do was to be known as a big data analytics company selling to large enterprises because for big data that's where the dollars are being spent right now.. What we did was we set down the mission, we set down the strategy and then the other piece, and this is sort of from my GE days when it comes to strategic execution, that we employed was you've got to have metrics that drive milestones in the journey.

    What we started to do was we tracked what percentage of our business came from mid-sized small companies versus large. Five years ago 0% came from large. Last quarter it was 75%. Then over this journey we would track that percentage of our business that came from these larger enterprises. The other thing we would track was in that fourth vector, the brand. How do you change the brand from being known as an open source BI company to being known as a big data analytics company? There we had again, at the best marketing organisation I've ever worked with that had a share of a voice metric. Not a feel-good, hey we had so many press releases, but a quantifiable metric about our brand that we tracked four years ago and it was what position do we play and what share of voice do we have when people talk about big data versus non big data.

    That was where our marketing team was very aggressive and had these metrics. When we first started out, since we launched ourselves as a big data analytics company we had a pretty good penetration in terms of the brand, but over the last couple years we've been tracking, we've been number one or two versus our competitors as the most identifiable brand in big data. That's a metric we track every month. Very, very quantifiable, but it's part of the journey. It took us a while to get there.

    Then the other piece, the other key metric for us is really the R and D investment and that was, we basically had to transform or re-engineer the project to really meet the needs of the large enterprise from a security standpoint, a scalability standpoint. Making sure that we integrate with all the key technologies that the large enterprise have and so that was again, when we did prioritization around out R and D we would prioritize and we'd have metrics around large enterprise and then we would sacrifice the needs of the small/medium in the product road map. That again was an evolution.

    Five years ago 10% of our R and D investment went into large enterprise features. Now that's the majority, it's something didn't happen overnight but we tracked and we shared with the company and sort of made it work.

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